{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c53eb8f5-80a7-4330-9f13-63220177fcc0",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "# Section 5.5 — Hierarchical models\n",
    "\n",
    "This notebook contains the code examples from [Section 5.5 Hierarchical models]() from the **No Bullshit Guide to Statistics**.\n",
    "\n",
    "See also:\n",
    "- [03_hierarchical_model.ipynb](http://localhost:8888/lab/tree/notebooks/explorations/bambi-paper-main/original/03_hierarchical_model.ipynb)\n",
    "- [cs109b_lect13_bayes_2_2021.ipynb](./explorations/cs109b_lect13_bayes_2_2021.ipynb)\n",
    "- https://github.com/fonnesbeck/pymc_sdss_2024/blob/main/notebooks/Section4-Hierarchical_Models.ipynb\n",
    "- https://mc-stan.org/users/documentation/case-studies/radon_cmdstanpy_plotnine.html#data-prep\n",
    "- https://github.com/mitzimorris/brms_feb_28_2023/blob/main/brms_notebook.Rmd\n",
    "- https://www.pymc.io/projects/examples/en/latest/generalized_linear_models/multilevel_modeling.html\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2d8dda2-58a9-424e-9fb3-32ad6e8777d8",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Notebook setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f80fd81f-9a56-4d15-ba41-3c1ebc2e69a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ensure required Python modules are installed\n",
    "# %pip install --quiet numpy scipy seaborn statsmodels bambi==0.15.0 pymc==5.23.0 ministats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aea0223e-aee9-4875-a714-897b6646baaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Python modules\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "efd86c5a-c9d2-4eab-b67d-a65e39b23ef2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figures setup\n",
    "plt.clf()  # needed otherwise `sns.set_theme` doesn't work\n",
    "sns.set_theme(\n",
    "    context=\"paper\",\n",
    "    style=\"whitegrid\",\n",
    "    palette=\"colorblind\",\n",
    "    rc={\"font.family\": \"serif\",\n",
    "        \"font.serif\": [\"Palatino\", \"DejaVu Serif\", \"serif\"],\n",
    "        \"figure.figsize\": (5,3)},\n",
    ")\n",
    "%config InlineBackend.figure_format = \"retina\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "df811a10-417d-4389-8bff-30e59b5f6aef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simple float __repr__  \n",
    "if int(np.__version__.split(\".\")[0]) >= 2:\n",
    "    np.set_printoptions(legacy='1.25')\n",
    "\n",
    "# Set random seed for repeatability\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "afa0951f-f497-402e-bd72-e03962e7d739",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datasets/ directory already exists.\n"
     ]
    }
   ],
   "source": [
    "# Download datasets/ directory if necessary\n",
    "from ministats import ensure_datasets\n",
    "ensure_datasets()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ede4334e-b30e-4cb2-b060-dce8a72a81b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# silence statsmodels kurtosistest warning when using n < 20\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8268b09a-6892-4617-9878-a58c78621628",
   "metadata": {},
   "outputs": [],
   "source": [
    "# silence ERROR messages when showing model graphs\n",
    "# cf. https://github.com/pymc-devs/pymc/issues/7901\n",
    "import logging\n",
    "logging.getLogger(\"pytensor.graph.rewriting.basic\").setLevel(logging.CRITICAL)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "511955ff-586b-4709-955b-62f971d102dc",
   "metadata": {},
   "source": [
    "## Definitions\n",
    "\n",
    "### Hierarchical (multilevel) models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d96a3759-bc32-4e68-836b-2be28d961e53",
   "metadata": {},
   "source": [
    "### Model formula\n",
    "\n",
    "A Bayesian hierarchical model is described by the following equations:\n",
    "\n",
    "$\\def\\calN{\\mathcal{N}}\n",
    " \\def\\Tdist{\\mathcal{T}}\n",
    " \\def\\Expon{\\mathrm{Expon}}$\n",
    "\n",
    "\\begin{align*}\n",
    "    X_j\t\t\t&\\sim\t\\calN(M_{j}, \\, \\Sigma_X),  \\\\\n",
    "    M_{j}\t\t&=\t\tB_0 + B_{0j},\t\t\t\t\t\\\\\n",
    "    \\Sigma_X\t&\\sim\t\\Tdist^+\\!(\\nu_{\\Sigma_X}, \\sigma_{\\Sigma_X}), \\\\\n",
    "    B_0\t\t\t&\\sim\t\\calN(\\mu_{B_0},\\sigma_{B_0}), \\\\\n",
    "    B_{0j}\t\t&\\sim\t\\calN(0,\\Sigma_{B_{0j}}), \\\\\n",
    "    \\Sigma_{B_{0j}}\t&\\sim\t\\Expon(\\lambda).\n",
    "\\end{align*}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de3143ac-ae88-4b1a-95a2-dccc9e83ab67",
   "metadata": {},
   "source": [
    "## Radon dataset\n",
    "\n",
    "https://bambinos.github.io/bambi/notebooks/radon_example.html\n",
    "\n",
    "- Description: Contains measurements of radon levels in homes across various counties.\n",
    "- Source: Featured in Andrew Gelman and Jennifer Hill's book Data Analysis Using Regression and Multilevel/Hierarchical Models.\n",
    "- Application: Demonstrates partial pooling and varying intercepts/slopes in hierarchical modeling.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac401559-b623-4804-a429-29f5b4322ab2",
   "metadata": {},
   "source": [
    "### Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b4dd6e28-ce71-44da-a393-03dd75df59f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(919, 6)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "radon = pd.read_csv(\"datasets/radon.csv\")\n",
    "radon.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e8c281bb-a6ef-4fd1-859c-194ed4a49342",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idnum</th>\n",
       "      <th>state</th>\n",
       "      <th>county</th>\n",
       "      <th>floor</th>\n",
       "      <th>log_radon</th>\n",
       "      <th>log_uranium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5081</td>\n",
       "      <td>MN</td>\n",
       "      <td>AITKIN</td>\n",
       "      <td>ground</td>\n",
       "      <td>0.788457</td>\n",
       "      <td>-0.689048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5082</td>\n",
       "      <td>MN</td>\n",
       "      <td>AITKIN</td>\n",
       "      <td>basement</td>\n",
       "      <td>0.788457</td>\n",
       "      <td>-0.689048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5083</td>\n",
       "      <td>MN</td>\n",
       "      <td>AITKIN</td>\n",
       "      <td>basement</td>\n",
       "      <td>1.064711</td>\n",
       "      <td>-0.689048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5084</td>\n",
       "      <td>MN</td>\n",
       "      <td>AITKIN</td>\n",
       "      <td>basement</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.689048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5085</td>\n",
       "      <td>MN</td>\n",
       "      <td>ANOKA</td>\n",
       "      <td>basement</td>\n",
       "      <td>1.131402</td>\n",
       "      <td>-0.847313</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   idnum state  county     floor  log_radon  log_uranium\n",
       "0   5081    MN  AITKIN    ground   0.788457    -0.689048\n",
       "1   5082    MN  AITKIN  basement   0.788457    -0.689048\n",
       "2   5083    MN  AITKIN  basement   1.064711    -0.689048\n",
       "3   5084    MN  AITKIN  basement   0.000000    -0.689048\n",
       "4   5085    MN   ANOKA  basement   1.131402    -0.847313"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "radon.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22d8ba90-3cba-4dcb-855d-28aba5770c40",
   "metadata": {},
   "source": [
    "### Descriptive statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a4130463-dd4f-44e9-bc07-1b1497181a5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    919.000000\n",
       "mean       1.224623\n",
       "std        0.853327\n",
       "min       -2.302585\n",
       "25%        0.641854\n",
       "50%        1.280934\n",
       "75%        1.791759\n",
       "max        3.875359\n",
       "Name: log_radon, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "radon[\"log_radon\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "74cb4944-7edd-41a4-b104-d722e37ce121",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "85"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(radon[\"county\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d6053da9-2a31-4ef7-8454-a1ba60e859d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "floor\n",
       "basement    766\n",
       "ground      153\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "radon[\"floor\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e2808259-e84e-4906-8881-2a3be7287f22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x400 with 8 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 369,
       "width": 848
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ministats.book.figures import plot_counties\n",
    "sel_counties = [\n",
    "  \"LAC QUI PARLE\", \"AITKIN\", \"KOOCHICHING\", \"DOUGLAS\",\n",
    "  \"HENNEPIN\", \"STEARNS\", \"RAMSEY\", \"ST LOUIS\",\n",
    "]\n",
    "plot_counties(radon, counties=sel_counties);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8be59664-bcd3-4d14-a055-f29368e8130a",
   "metadata": {},
   "source": [
    "## Example 1: complete pooling model\n",
    "\n",
    "= common linear regression model for all counties"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cd75ef9-01b3-472c-8cc9-d8fa5b9f3bf6",
   "metadata": {},
   "source": [
    "### Bayesian model\n",
    "\n",
    "\n",
    "We can  pool all the data and estimate one big regression to asses the influence of the floor variable\n",
    "on radon levels across all counties.\n",
    "\n",
    "\\begin{align*}\n",
    "    R\t\t\t&\\sim\t\\calN(M_R, \\, \\Sigma_R),  \t\\\\\n",
    "    M_R\t\t\t&=\t\tB_0 + B_{\\!f}\\!\\cdot\\!f,\t\\\\\n",
    "    \\Sigma_R\t&\\sim\t\\Tdist^+\\!(4, 1),\t\t\t\\\\\n",
    "    B_0\t\t\t&\\sim\t\\calN(1, 2), \t\t\t\t\\\\\n",
    "    B_f\t\t\t&\\sim\t\\calN(0, 5).\n",
    "\\end{align*}\n",
    "\n",
    "The variable $f$ corresponds to the column `floor` in the `radon` data frame,\n",
    "which will be internally coded as binary\n",
    "with $0$ representing basement,\n",
    "and $1$ representing ground floor.\n",
    "\n",
    "By ignoring the county feature, we do not differenciate on counties."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e15ce671-449c-4d9b-be6f-e6c75dd87dba",
   "metadata": {},
   "source": [
    "### Bambi model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "41700bcb-a5b6-404f-93de-d334f66de424",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       Formula: log_radon ~ 1 + floor\n",
       "        Family: gaussian\n",
       "          Link: mu = identity\n",
       "  Observations: 919\n",
       "        Priors: \n",
       "    target = mu\n",
       "        Common-level effects\n",
       "            Intercept ~ Normal(mu: 1.0, sigma: 2.0)\n",
       "            floor ~ Normal(mu: 0.0, sigma: 5.0)\n",
       "        \n",
       "        Auxiliary parameters\n",
       "            sigma ~ HalfStudentT(nu: 4.0, sigma: 1.0)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import bambi as bmb\n",
    "\n",
    "priors1 = {\n",
    "    \"Intercept\": bmb.Prior(\"Normal\", mu=1, sigma=2),\n",
    "    \"floor\": bmb.Prior(\"Normal\", mu=0, sigma=5),\n",
    "    \"sigma\": bmb.Prior(\"HalfStudentT\", nu=4, sigma=1),\n",
    "}\n",
    "\n",
    "mod1 = bmb.Model(formula=\"log_radon ~ 1 + floor\",\n",
    "                 family=\"gaussian\",\n",
    "                 link=\"identity\",\n",
    "                 priors=priors1,\n",
    "                 data=radon)\n",
    "mod1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6b116815-83cc-47a7-b7c1-6294366337ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 12.1.0 (20240811.2233)\n",
       " -->\n",
       "<!-- Pages: 1 -->\n",
       "<svg width=\"266pt\" height=\"385pt\"\n",
       " viewBox=\"0.00 0.00 266.29 384.95\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 380.95)\">\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-380.95 262.29,-380.95 262.29,4 -4,4\"/>\n",
       "<g id=\"clust1\" class=\"cluster\">\n",
       "<title>clusterfloor_dim (1)</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M159.29,-247.13C159.29,-247.13 233.29,-247.13 233.29,-247.13 239.29,-247.13 245.29,-253.13 245.29,-259.13 245.29,-259.13 245.29,-356.95 245.29,-356.95 245.29,-362.95 239.29,-368.95 233.29,-368.95 233.29,-368.95 159.29,-368.95 159.29,-368.95 153.29,-368.95 147.29,-362.95 147.29,-356.95 147.29,-356.95 147.29,-259.13 147.29,-259.13 147.29,-253.13 153.29,-247.13 159.29,-247.13\"/>\n",
       "<text text-anchor=\"middle\" x=\"200.16\" y=\"-254.33\" font-family=\"Times,serif\" font-size=\"14.00\">floor_dim (1)</text>\n",
       "</g>\n",
       "<g id=\"clust2\" class=\"cluster\">\n",
       "<title>cluster__obs__ (919)</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M146.29,-8C146.29,-8 238.29,-8 238.29,-8 244.29,-8 250.29,-14 250.29,-20 250.29,-20 250.29,-223.23 250.29,-223.23 250.29,-229.23 244.29,-235.23 238.29,-235.23 238.29,-235.23 146.29,-235.23 146.29,-235.23 140.29,-235.23 134.29,-229.23 134.29,-223.23 134.29,-223.23 134.29,-20 134.29,-20 134.29,-14 140.29,-8 146.29,-8\"/>\n",
       "<text text-anchor=\"middle\" x=\"202.91\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">__obs__ (919)</text>\n",
       "</g>\n",
       "<!-- sigma -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>sigma</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"63.29\" cy=\"-198.48\" rx=\"63.29\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"63.29\" y=\"-209.93\" font-family=\"Times,serif\" font-size=\"14.00\">sigma</text>\n",
       "<text text-anchor=\"middle\" x=\"63.29\" y=\"-193.43\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"63.29\" y=\"-176.93\" font-family=\"Times,serif\" font-size=\"14.00\">HalfStudentT</text>\n",
       "</g>\n",
       "<!-- log_radon -->\n",
       "<g id=\"node4\" class=\"node\">\n",
       "<title>log_radon</title>\n",
       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"192.29\" cy=\"-81.16\" rx=\"50.03\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"192.29\" y=\"-92.61\" font-family=\"Times,serif\" font-size=\"14.00\">log_radon</text>\n",
       "<text text-anchor=\"middle\" x=\"192.29\" y=\"-76.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"192.29\" y=\"-59.61\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- sigma&#45;&gt;log_radon -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>sigma&#45;&gt;log_radon</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M99.56,-165.05C115.43,-150.86 134.13,-134.15 150.57,-119.45\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"152.68,-122.26 157.8,-112.99 148.01,-117.04 152.68,-122.26\"/>\n",
       "</g>\n",
       "<!-- Intercept -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>Intercept</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"92.29\" cy=\"-320.29\" rx=\"45.25\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"92.29\" y=\"-331.74\" font-family=\"Times,serif\" font-size=\"14.00\">Intercept</text>\n",
       "<text text-anchor=\"middle\" x=\"92.29\" y=\"-315.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"92.29\" y=\"-298.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- mu -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>mu</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"237.41,-227.23 147.16,-227.23 147.16,-169.73 237.41,-169.73 237.41,-227.23\"/>\n",
       "<text text-anchor=\"middle\" x=\"192.29\" y=\"-209.93\" font-family=\"Times,serif\" font-size=\"14.00\">mu</text>\n",
       "<text text-anchor=\"middle\" x=\"192.29\" y=\"-193.43\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"192.29\" y=\"-176.93\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
       "</g>\n",
       "<!-- Intercept&#45;&gt;mu -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>Intercept&#45;&gt;mu</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M114.92,-284.55C123.36,-272.36 133.32,-258.8 143.29,-247.13 146.54,-243.33 150.05,-239.48 153.65,-235.71\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"156.02,-238.29 160.54,-228.7 151.03,-233.38 156.02,-238.29\"/>\n",
       "</g>\n",
       "<!-- floor -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>floor</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"196.29\" cy=\"-320.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"196.29\" y=\"-331.74\" font-family=\"Times,serif\" font-size=\"14.00\">floor</text>\n",
       "<text text-anchor=\"middle\" x=\"196.29\" y=\"-315.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"196.29\" y=\"-298.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- floor&#45;&gt;mu -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>floor&#45;&gt;mu</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M194.95,-279.4C194.52,-266.37 194.04,-251.91 193.6,-238.91\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"197.1,-238.86 193.27,-228.98 190.11,-239.09 197.1,-238.86\"/>\n",
       "</g>\n",
       "<!-- mu&#45;&gt;log_radon -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>mu&#45;&gt;log_radon</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M192.29,-169.27C192.29,-158.44 192.29,-145.73 192.29,-133.41\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"195.79,-133.78 192.29,-123.78 188.79,-133.78 195.79,-133.78\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.graphs.Digraph at 0x11f393b90>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod1.build()\n",
    "mod1.graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91411ab7-c749-448e-be07-d30f0b53e1de",
   "metadata": {},
   "source": [
    "### Model fitting and analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2d288dae-83d8-4ae6-9b7e-4d3ce4f9e920",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [sigma, Intercept, floor]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ee9da8a746ca4b18893d7a28def01eb9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 1 seconds.\n"
     ]
    }
   ],
   "source": [
    "idata1 = mod1.fit(random_seed=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f5ce6bb9-8411-46e2-932a-44e69801577b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>0.824</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.787</td>\n",
       "      <td>0.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>1.327</td>\n",
       "      <td>0.030</td>\n",
       "      <td>1.271</td>\n",
       "      <td>1.383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floor[ground]</th>\n",
       "      <td>-0.615</td>\n",
       "      <td>0.074</td>\n",
       "      <td>-0.754</td>\n",
       "      <td>-0.477</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                mean     sd  hdi_3%  hdi_97%\n",
       "sigma          0.824  0.020   0.787    0.860\n",
       "Intercept      1.327  0.030   1.271    1.383\n",
       "floor[ground] -0.615  0.074  -0.754   -0.477"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import arviz as az\n",
    "\n",
    "az.summary(idata1, kind=\"stats\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4c78a306-644a-4b2b-9cae-f6a9dc888083",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1725x345 with 3 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 338,
       "width": 1356
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_posterior(idata1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "baac68f5-c6e7-4c12-ae02-50b1618c19cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Default computed for conditional variable: floor\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 293,
       "width": 456
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = bmb.interpret.plot_predictions(mod1, idata1, conditional=\"floor\")\n",
    "\n",
    "means1 = az.summary(idata1, kind=\"stats\")[\"mean\"]\n",
    "y0 = means1[\"Intercept\"]\n",
    "y1 = means1[\"Intercept\"] + means1[\"floor[ground]\"]\n",
    "sns.lineplot(x=[0,1], y=[y0,y1], ax=axs[0]);\n",
    "\n",
    "midpoint = [0.5, (y0+y1)/2 + 0.03]\n",
    "slope = means1[\"floor[ground]\"].round(2)\n",
    "axs[0].annotate(\"$\\\\mu_{B_{f}}=%.2f$\" % slope, midpoint);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b1e21da-9bb6-4712-a3c0-d502b00ec609",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "not using group membership, so we have lots of bias\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66688e81-9a03-4b6e-b567-b66ed3bfbbad",
   "metadata": {},
   "source": [
    "## Example 2: no pooling model\n",
    "\n",
    "= separate intercept for each county "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21a8b689-cc1b-4c1e-bf19-ee8d67f213f4",
   "metadata": {},
   "source": [
    "### Bayesian model\n",
    "\n",
    "If we treat different counties as independent,\n",
    "so each one gets an intercept term:\n",
    "\n",
    "\\begin{align*}\n",
    "    R_j\t\t\t&\\sim\t\\calN(M_j, \\, \\Sigma_R),  \t\t\t\t\t\\\\\n",
    "    M_j\t\t\t&=\t\tB_{0j} + B_{\\!f}\\!\\cdot\\!f,\t\t\t\t\t\\\\\n",
    "    \\Sigma_R\t\t&\\sim\t\\Tdist^+\\!(4, 1),\t\t\t\t\t\t\t\\\\\n",
    "    B_{0j}\t\t&\\sim\t\\calN(1, 2),\t\t\t\t\t\t\t\\\\\n",
    "    B_f\t\t\t&\\sim\t\\calN(0, 5).\n",
    "\\end{align*}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90782a3c-31d9-400d-9f91-7d9e56335b69",
   "metadata": {},
   "source": [
    "### Bambi model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "170bd5a2-ada6-49a9-b93e-8cb4ab204955",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       Formula: log_radon ~ 0 + county + floor\n",
       "        Family: gaussian\n",
       "          Link: mu = identity\n",
       "  Observations: 919\n",
       "        Priors: \n",
       "    target = mu\n",
       "        Common-level effects\n",
       "            county ~ Normal(mu: 1.0, sigma: 2.0)\n",
       "            floor ~ Normal(mu: 0.0, sigma: 5.0)\n",
       "        \n",
       "        Auxiliary parameters\n",
       "            sigma ~ HalfStudentT(nu: 4.0, sigma: 1.0)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "priors2 = {\n",
    "    \"county\": bmb.Prior(\"Normal\", mu=1, sigma=2),\n",
    "    \"floor\": bmb.Prior(\"Normal\", mu=0, sigma=5),\n",
    "    \"sigma\": bmb.Prior(\"HalfStudentT\", nu=4, sigma=1),\n",
    "}\n",
    "\n",
    "mod2 = bmb.Model(\"log_radon ~ 0 + county + floor\",\n",
    "                 family=\"gaussian\",\n",
    "                 link=\"identity\",\n",
    "                 priors=priors2,\n",
    "                 data=radon)\n",
    "mod2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ff10550d-548e-438f-b737-bd54905862ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 12.1.0 (20240811.2233)\n",
       " -->\n",
       "<!-- Pages: 1 -->\n",
       "<svg width=\"312pt\" height=\"385pt\"\n",
       " viewBox=\"0.00 0.00 312.29 384.95\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 380.95)\">\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-380.95 308.29,-380.95 308.29,4 -4,4\"/>\n",
       "<g id=\"clust1\" class=\"cluster\">\n",
       "<title>clustercounty_dim (85)</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M95.29,-247.13C95.29,-247.13 178.29,-247.13 178.29,-247.13 184.29,-247.13 190.29,-253.13 190.29,-259.13 190.29,-259.13 190.29,-356.95 190.29,-356.95 190.29,-362.95 184.29,-368.95 178.29,-368.95 178.29,-368.95 95.29,-368.95 95.29,-368.95 89.29,-368.95 83.29,-362.95 83.29,-356.95 83.29,-356.95 83.29,-259.13 83.29,-259.13 83.29,-253.13 89.29,-247.13 95.29,-247.13\"/>\n",
       "<text text-anchor=\"middle\" x=\"136.54\" y=\"-254.33\" font-family=\"Times,serif\" font-size=\"14.00\">county_dim (85)</text>\n",
       "</g>\n",
       "<g id=\"clust2\" class=\"cluster\">\n",
       "<title>clusterfloor_dim (1)</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M210.29,-247.13C210.29,-247.13 284.29,-247.13 284.29,-247.13 290.29,-247.13 296.29,-253.13 296.29,-259.13 296.29,-259.13 296.29,-356.95 296.29,-356.95 296.29,-362.95 290.29,-368.95 284.29,-368.95 284.29,-368.95 210.29,-368.95 210.29,-368.95 204.29,-368.95 198.29,-362.95 198.29,-356.95 198.29,-356.95 198.29,-259.13 198.29,-259.13 198.29,-253.13 204.29,-247.13 210.29,-247.13\"/>\n",
       "<text text-anchor=\"middle\" x=\"251.16\" y=\"-254.33\" font-family=\"Times,serif\" font-size=\"14.00\">floor_dim (1)</text>\n",
       "</g>\n",
       "<g id=\"clust3\" class=\"cluster\">\n",
       "<title>cluster__obs__ (919)</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M146.29,-8C146.29,-8 238.29,-8 238.29,-8 244.29,-8 250.29,-14 250.29,-20 250.29,-20 250.29,-223.23 250.29,-223.23 250.29,-229.23 244.29,-235.23 238.29,-235.23 238.29,-235.23 146.29,-235.23 146.29,-235.23 140.29,-235.23 134.29,-229.23 134.29,-223.23 134.29,-223.23 134.29,-20 134.29,-20 134.29,-14 140.29,-8 146.29,-8\"/>\n",
       "<text text-anchor=\"middle\" x=\"202.91\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">__obs__ (919)</text>\n",
       "</g>\n",
       "<!-- sigma -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>sigma</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"63.29\" cy=\"-198.48\" rx=\"63.29\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"63.29\" y=\"-209.93\" font-family=\"Times,serif\" font-size=\"14.00\">sigma</text>\n",
       "<text text-anchor=\"middle\" x=\"63.29\" y=\"-193.43\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"63.29\" y=\"-176.93\" font-family=\"Times,serif\" font-size=\"14.00\">HalfStudentT</text>\n",
       "</g>\n",
       "<!-- log_radon -->\n",
       "<g id=\"node4\" class=\"node\">\n",
       "<title>log_radon</title>\n",
       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"192.29\" cy=\"-81.16\" rx=\"50.03\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"192.29\" y=\"-92.61\" font-family=\"Times,serif\" font-size=\"14.00\">log_radon</text>\n",
       "<text text-anchor=\"middle\" x=\"192.29\" y=\"-76.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"192.29\" y=\"-59.61\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- sigma&#45;&gt;log_radon -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>sigma&#45;&gt;log_radon</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M99.56,-165.05C115.43,-150.86 134.13,-134.15 150.57,-119.45\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"152.68,-122.26 157.8,-112.99 148.01,-117.04 152.68,-122.26\"/>\n",
       "</g>\n",
       "<!-- county -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>county</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"141.29\" cy=\"-320.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"141.29\" y=\"-331.74\" font-family=\"Times,serif\" font-size=\"14.00\">county</text>\n",
       "<text text-anchor=\"middle\" x=\"141.29\" y=\"-315.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"141.29\" y=\"-298.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- mu -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>mu</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"237.41,-227.23 147.16,-227.23 147.16,-169.73 237.41,-169.73 237.41,-227.23\"/>\n",
       "<text text-anchor=\"middle\" x=\"192.29\" y=\"-209.93\" font-family=\"Times,serif\" font-size=\"14.00\">mu</text>\n",
       "<text text-anchor=\"middle\" x=\"192.29\" y=\"-193.43\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"192.29\" y=\"-176.93\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
       "</g>\n",
       "<!-- county&#45;&gt;mu -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>county&#45;&gt;mu</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M157,-282.37C162.97,-268.34 169.79,-252.33 175.83,-238.15\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"178.98,-239.68 179.67,-229.11 172.53,-236.94 178.98,-239.68\"/>\n",
       "</g>\n",
       "<!-- floor -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>floor</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"247.29\" cy=\"-320.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"247.29\" y=\"-331.74\" font-family=\"Times,serif\" font-size=\"14.00\">floor</text>\n",
       "<text text-anchor=\"middle\" x=\"247.29\" y=\"-315.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"247.29\" y=\"-298.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- floor&#45;&gt;mu -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>floor&#45;&gt;mu</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M230.49,-282.69C223.93,-268.41 216.41,-252.02 209.78,-237.59\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"213.13,-236.49 205.78,-228.86 206.77,-239.41 213.13,-236.49\"/>\n",
       "</g>\n",
       "<!-- mu&#45;&gt;log_radon -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>mu&#45;&gt;log_radon</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M192.29,-169.27C192.29,-158.44 192.29,-145.73 192.29,-133.41\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"195.79,-133.78 192.29,-123.78 188.79,-133.78 195.79,-133.78\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.graphs.Digraph at 0x13a148aa0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod2.build()\n",
    "mod2.graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c65a0e81-96ff-48a2-92af-f2ba4630a06e",
   "metadata": {},
   "source": [
    "### Model fitting and analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "360e736c-3be0-41a9-8e66-c6cd4202095c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [sigma, county, floor]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7271c3fba59b4f30b2ff7b313bccb185",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 2 seconds.\n"
     ]
    }
   ],
   "source": [
    "idata2 = mod2.fit(random_seed=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dee502b8-cd3d-4278-9ab6-d4f3182da629",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>0.756</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.720</td>\n",
       "      <td>0.791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>county[LAC QUI PARLE]</th>\n",
       "      <td>2.832</td>\n",
       "      <td>0.531</td>\n",
       "      <td>1.859</td>\n",
       "      <td>3.884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>county[AITKIN]</th>\n",
       "      <td>0.838</td>\n",
       "      <td>0.376</td>\n",
       "      <td>0.149</td>\n",
       "      <td>1.555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>county[KOOCHICHING]</th>\n",
       "      <td>0.814</td>\n",
       "      <td>0.288</td>\n",
       "      <td>0.298</td>\n",
       "      <td>1.388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>county[DOUGLAS]</th>\n",
       "      <td>1.717</td>\n",
       "      <td>0.241</td>\n",
       "      <td>1.264</td>\n",
       "      <td>2.167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>county[HENNEPIN]</th>\n",
       "      <td>1.360</td>\n",
       "      <td>0.074</td>\n",
       "      <td>1.223</td>\n",
       "      <td>1.503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>county[STEARNS]</th>\n",
       "      <td>1.484</td>\n",
       "      <td>0.150</td>\n",
       "      <td>1.205</td>\n",
       "      <td>1.764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>county[RAMSEY]</th>\n",
       "      <td>1.156</td>\n",
       "      <td>0.131</td>\n",
       "      <td>0.901</td>\n",
       "      <td>1.401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>county[ST LOUIS]</th>\n",
       "      <td>0.865</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.733</td>\n",
       "      <td>1.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floor[ground]</th>\n",
       "      <td>-0.707</td>\n",
       "      <td>0.073</td>\n",
       "      <td>-0.849</td>\n",
       "      <td>-0.573</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        mean     sd  hdi_3%  hdi_97%\n",
       "sigma                  0.756  0.019   0.720    0.791\n",
       "county[LAC QUI PARLE]  2.832  0.531   1.859    3.884\n",
       "county[AITKIN]         0.838  0.376   0.149    1.555\n",
       "county[KOOCHICHING]    0.814  0.288   0.298    1.388\n",
       "county[DOUGLAS]        1.717  0.241   1.264    2.167\n",
       "county[HENNEPIN]       1.360  0.074   1.223    1.503\n",
       "county[STEARNS]        1.484  0.150   1.205    1.764\n",
       "county[RAMSEY]         1.156  0.131   0.901    1.401\n",
       "county[ST LOUIS]       0.865  0.072   0.733    1.008\n",
       "floor[ground]         -0.707  0.073  -0.849   -0.573"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idata2sel = idata2.sel(county_dim=sel_counties)\n",
    "az.summary(idata2sel, kind=\"stats\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0bb84448-3582-4e1b-80e6-58d41b293216",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x260 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 243,
       "width": 643
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axs = az.plot_forest(idata2sel, combined=True, figsize=(6,2.6))\n",
    "axs[0].set_xticks(np.arange(-0.5,4.6,0.5))\n",
    "axs[0].set_title(None);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "92714b2d-1103-4147-8287-6262074faea3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x400 with 8 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 369,
       "width": 848
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_counties(radon, idata_cp=idata1, idata_np=idata2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "452ee230-701b-4205-902a-58a5413cdb30",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig, axs = bmb.interpret.plot_predictions(mod2, idata2, [\"floor\", \"county\"]);\n",
    "# axs[0].get_legend().remove()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "aa7af2ff-f236-4acc-91f0-cca809fdfbe7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# post2 = idata2[\"posterior\"]\n",
    "# unpooled_means = post2.mean(dim=(\"chain\", \"draw\"))\n",
    "# unpooled_hdi = az.hdi(idata2)\n",
    "\n",
    "# unpooled_means_iter = unpooled_means.sortby(\"county\")\n",
    "# unpooled_hdi_iter = unpooled_hdi.sortby(unpooled_means_iter.county)\n",
    "\n",
    "# _, ax = plt.subplots(figsize=(12, 5))\n",
    "# unpooled_means_iter.plot.scatter(x=\"county_dim\", y=\"county\", ax=ax, alpha=0.9)\n",
    "# ax.vlines(\n",
    "#     np.arange(len(radon[\"county\"].unique())),\n",
    "#     unpooled_hdi_iter.county.sel(hdi=\"lower\"),\n",
    "#     unpooled_hdi_iter.county.sel(hdi=\"higher\"),\n",
    "#     color=\"orange\",\n",
    "#     alpha=0.6,\n",
    "# )\n",
    "# ax.set(ylabel=\"Radon estimate\", ylim=(-2, 4.5))\n",
    "# ax.tick_params(rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de728f3d-f7ee-4a82-8746-e622c8242e30",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "treating each group independently, so we have lots of variance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a34dbc5b-8701-4452-a172-f91e60c8e627",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO:\n",
    "# calculate the std.dev. of the county-specific slopes -- this is like \\Sigma_J but w/o a prior."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98c50c0c-8035-4f71-9a9a-cee195cd47a6",
   "metadata": {},
   "source": [
    "## Example 3: hierarchical model\n",
    "\n",
    "= partial pooling model\n",
    "= varying intercepts model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e212d81-a2a9-4696-8cc5-fe9a6dcb12a4",
   "metadata": {},
   "source": [
    "### Bayesian hierarchical model\n",
    "\n",
    "\n",
    "\\begin{align*}\n",
    "    R_j\t\t\t&\\sim\t\\calN(M_j, \\, \\Sigma_R),\t\t\t\t\t\t\\\\\n",
    "    M_j\t\t\t&=\t\tB_0 + B_{0j} + B_f\\!\\cdot\\!f,\t\t\t\t\t\\\\\n",
    "    \\Sigma_R\t&\\sim\t\\Tdist^+\\!(4, 1),\t\t\t\t\t\t\t\t\\\\\n",
    "    B_0\t\t\t&\\sim\t\\calN(1,2),\t\t\t\t\t\t\t\t\t\t\\\\\n",
    "    B_{0j}\t\t&\\sim\t\\calN(0,\\Sigma_{B_{0j}}),\t\t\t\t\t\t\\\\\n",
    "    B_f\t\t\t&\\sim\t\\calN(0, 5),\t\t\t\t\t\t\t\t\t\\\\\n",
    "    \\Sigma_{B_{0j}}\t&\\sim\t\\Expon(1).\n",
    "\\end{align*}\n",
    "\n",
    "\n",
    "The partial pooling formula estimates per-county intercepts which drawn\n",
    "from the same distribution which is estimated jointly with the rest of\n",
    "the model parameters. The `1` is the intercept co-efficient. The\n",
    "estimates across counties will all have the same slope.\n",
    "\n",
    "\n",
    "    log_radon ~ 1 + (1|county_id) + floor\n",
    "\n",
    "\n",
    "There is a middle ground to both of these extremes.\n",
    "Specifically, we may assume that the intercepts are different for each county as in the unpooled case,\n",
    "but they are drawn from the same distribution.\n",
    "The different counties are effectively sharing information through the common prior.\n",
    "\n",
    "\n",
    "NOTE: some counties have very few sample; the hierarchical model will provide \"shrinkage\" for these groups, and use global information learned from all counties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "97ff9245-6ef6-4e4d-b495-9384ef25e707",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    919.000000\n",
       "mean       1.224623\n",
       "std        0.853327\n",
       "min       -2.302585\n",
       "25%        0.641854\n",
       "50%        1.280934\n",
       "75%        1.791759\n",
       "max        3.875359\n",
       "Name: log_radon, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "radon[\"log_radon\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dfbd62d3-70a2-46b4-b7f3-8c0ad7cd3a48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floor</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>basement</th>\n",
       "      <td>766.0</td>\n",
       "      <td>1.326744</td>\n",
       "      <td>0.782709</td>\n",
       "      <td>-2.302585</td>\n",
       "      <td>0.788457</td>\n",
       "      <td>1.360977</td>\n",
       "      <td>1.883253</td>\n",
       "      <td>3.875359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ground</th>\n",
       "      <td>153.0</td>\n",
       "      <td>0.713349</td>\n",
       "      <td>0.999376</td>\n",
       "      <td>-2.302585</td>\n",
       "      <td>0.095310</td>\n",
       "      <td>0.741937</td>\n",
       "      <td>1.308333</td>\n",
       "      <td>3.234749</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count      mean       std       min       25%       50%       75%  \\\n",
       "floor                                                                         \n",
       "basement  766.0  1.326744  0.782709 -2.302585  0.788457  1.360977  1.883253   \n",
       "ground    153.0  0.713349  0.999376 -2.302585  0.095310  0.741937  1.308333   \n",
       "\n",
       "               max  \n",
       "floor               \n",
       "basement  3.875359  \n",
       "ground    3.234749  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "radon.groupby(\"floor\")[\"log_radon\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f3e0d9d-3710-45a2-83ab-0565aab76575",
   "metadata": {},
   "source": [
    "### Bambi model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "072fb211-5266-4589-8ee6-37bd4f909764",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       Formula: log_radon ~ 1 + (1|county) + floor\n",
       "        Family: gaussian\n",
       "          Link: mu = identity\n",
       "  Observations: 919\n",
       "        Priors: \n",
       "    target = mu\n",
       "        Common-level effects\n",
       "            Intercept ~ Normal(mu: 1.0, sigma: 2.0)\n",
       "            floor ~ Normal(mu: 0.0, sigma: 5.0)\n",
       "        \n",
       "        Group-level effects\n",
       "            1|county ~ Normal(mu: 0.0, sigma: Exponential(lam: 1.0))\n",
       "        \n",
       "        Auxiliary parameters\n",
       "            sigma ~ HalfStudentT(nu: 4.0, sigma: 1.0)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "priors3 = {\n",
    "    \"Intercept\": bmb.Prior(\"Normal\", mu=1, sigma=2),\n",
    "    \"floor\": bmb.Prior(\"Normal\", mu=0, sigma=5),\n",
    "    \"1|county\": bmb.Prior(\"Normal\", mu=0, sigma=bmb.Prior(\"Exponential\", lam=1)),\n",
    "    \"sigma\": bmb.Prior(\"HalfStudentT\", nu=4, sigma=1),\n",
    "}\n",
    "\n",
    "formula3 = \"log_radon ~ 1 + (1|county) + floor\"\n",
    "mod3 = bmb.Model(formula=formula3,\n",
    "                 family=\"gaussian\",\n",
    "                 link=\"identity\",\n",
    "                 priors=priors3,\n",
    "                 data=radon,\n",
    "                 noncentered=False)\n",
    "\n",
    "mod3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fdb3955d-987b-44fb-a1e1-98ec2547e206",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 12.1.0 (20240811.2233)\n",
       " -->\n",
       "<!-- Pages: 1 -->\n",
       "<svg width=\"417pt\" height=\"486pt\"\n",
       " viewBox=\"0.00 0.00 417.29 486.27\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 482.27)\">\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-482.27 413.29,-482.27 413.29,4 -4,4\"/>\n",
       "<g id=\"clust1\" class=\"cluster\">\n",
       "<title>clusterfloor_dim (1)</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M155.29,-247.13C155.29,-247.13 229.29,-247.13 229.29,-247.13 235.29,-247.13 241.29,-253.13 241.29,-259.13 241.29,-259.13 241.29,-356.95 241.29,-356.95 241.29,-362.95 235.29,-368.95 229.29,-368.95 229.29,-368.95 155.29,-368.95 155.29,-368.95 149.29,-368.95 143.29,-362.95 143.29,-356.95 143.29,-356.95 143.29,-259.13 143.29,-259.13 143.29,-253.13 149.29,-247.13 155.29,-247.13\"/>\n",
       "<text text-anchor=\"middle\" x=\"196.16\" y=\"-254.33\" font-family=\"Times,serif\" font-size=\"14.00\">floor_dim (1)</text>\n",
       "</g>\n",
       "<g id=\"clust2\" class=\"cluster\">\n",
       "<title>clustercounty__factor_dim (85)</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M261.29,-247.13C261.29,-247.13 389.29,-247.13 389.29,-247.13 395.29,-247.13 401.29,-253.13 401.29,-259.13 401.29,-259.13 401.29,-356.95 401.29,-356.95 401.29,-362.95 395.29,-368.95 389.29,-368.95 389.29,-368.95 261.29,-368.95 261.29,-368.95 255.29,-368.95 249.29,-362.95 249.29,-356.95 249.29,-356.95 249.29,-259.13 249.29,-259.13 249.29,-253.13 255.29,-247.13 261.29,-247.13\"/>\n",
       "<text text-anchor=\"middle\" x=\"325.04\" y=\"-254.33\" font-family=\"Times,serif\" font-size=\"14.00\">county__factor_dim (85)</text>\n",
       "</g>\n",
       "<g id=\"clust3\" class=\"cluster\">\n",
       "<title>cluster__obs__ (919)</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M146.29,-8C146.29,-8 238.29,-8 238.29,-8 244.29,-8 250.29,-14 250.29,-20 250.29,-20 250.29,-223.23 250.29,-223.23 250.29,-229.23 244.29,-235.23 238.29,-235.23 238.29,-235.23 146.29,-235.23 146.29,-235.23 140.29,-235.23 134.29,-229.23 134.29,-223.23 134.29,-223.23 134.29,-20 134.29,-20 134.29,-14 140.29,-8 146.29,-8\"/>\n",
       "<text text-anchor=\"middle\" x=\"202.91\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">__obs__ (919)</text>\n",
       "</g>\n",
       "<!-- sigma -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>sigma</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"63.29\" cy=\"-198.48\" rx=\"63.29\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"63.29\" y=\"-209.93\" font-family=\"Times,serif\" font-size=\"14.00\">sigma</text>\n",
       "<text text-anchor=\"middle\" x=\"63.29\" y=\"-193.43\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"63.29\" y=\"-176.93\" font-family=\"Times,serif\" font-size=\"14.00\">HalfStudentT</text>\n",
       "</g>\n",
       "<!-- log_radon -->\n",
       "<g id=\"node6\" class=\"node\">\n",
       "<title>log_radon</title>\n",
       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"192.29\" cy=\"-81.16\" rx=\"50.03\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"192.29\" y=\"-92.61\" font-family=\"Times,serif\" font-size=\"14.00\">log_radon</text>\n",
       "<text text-anchor=\"middle\" x=\"192.29\" y=\"-76.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"192.29\" y=\"-59.61\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- sigma&#45;&gt;log_radon -->\n",
       "<g id=\"edge5\" class=\"edge\">\n",
       "<title>sigma&#45;&gt;log_radon</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M99.56,-165.05C115.43,-150.86 134.13,-134.15 150.57,-119.45\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"152.68,-122.26 157.8,-112.99 148.01,-117.04 152.68,-122.26\"/>\n",
       "</g>\n",
       "<!-- 1|county_sigma -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>1|county_sigma</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"301.29\" cy=\"-437.61\" rx=\"72.3\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"301.29\" y=\"-449.06\" font-family=\"Times,serif\" font-size=\"14.00\">1|county_sigma</text>\n",
       "<text text-anchor=\"middle\" x=\"301.29\" y=\"-432.56\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"301.29\" y=\"-416.06\" font-family=\"Times,serif\" font-size=\"14.00\">Exponential</text>\n",
       "</g>\n",
       "<!-- 1|county -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>1|county</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"301.29\" cy=\"-320.29\" rx=\"44.19\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"301.29\" y=\"-331.74\" font-family=\"Times,serif\" font-size=\"14.00\">1|county</text>\n",
       "<text text-anchor=\"middle\" x=\"301.29\" y=\"-315.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"301.29\" y=\"-298.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- 1|county_sigma&#45;&gt;1|county -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>1|county_sigma&#45;&gt;1|county</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M301.29,-396.6C301.29,-388.82 301.29,-380.56 301.29,-372.48\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"304.79,-372.74 301.29,-362.74 297.79,-372.74 304.79,-372.74\"/>\n",
       "</g>\n",
       "<!-- Intercept -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>Intercept</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"88.29\" cy=\"-320.29\" rx=\"45.25\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"88.29\" y=\"-331.74\" font-family=\"Times,serif\" font-size=\"14.00\">Intercept</text>\n",
       "<text text-anchor=\"middle\" x=\"88.29\" y=\"-315.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"88.29\" y=\"-298.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- mu -->\n",
       "<g id=\"node7\" class=\"node\">\n",
       "<title>mu</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"237.41,-227.23 147.16,-227.23 147.16,-169.73 237.41,-169.73 237.41,-227.23\"/>\n",
       "<text text-anchor=\"middle\" x=\"192.29\" y=\"-209.93\" font-family=\"Times,serif\" font-size=\"14.00\">mu</text>\n",
       "<text text-anchor=\"middle\" x=\"192.29\" y=\"-193.43\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"192.29\" y=\"-176.93\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
       "</g>\n",
       "<!-- Intercept&#45;&gt;mu -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>Intercept&#45;&gt;mu</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M110.41,-284.7C118.88,-272.39 128.99,-258.71 139.29,-247.13 142.8,-243.18 146.62,-239.22 150.54,-235.36\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"152.75,-238.1 157.58,-228.67 147.92,-233.02 152.75,-238.1\"/>\n",
       "</g>\n",
       "<!-- floor -->\n",
       "<g id=\"node4\" class=\"node\">\n",
       "<title>floor</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"192.29\" cy=\"-320.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"192.29\" y=\"-331.74\" font-family=\"Times,serif\" font-size=\"14.00\">floor</text>\n",
       "<text text-anchor=\"middle\" x=\"192.29\" y=\"-315.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"192.29\" y=\"-298.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- floor&#45;&gt;mu -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>floor&#45;&gt;mu</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M192.29,-279.4C192.29,-266.37 192.29,-251.91 192.29,-238.91\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"195.79,-238.98 192.29,-228.98 188.79,-238.98 195.79,-238.98\"/>\n",
       "</g>\n",
       "<!-- 1|county&#45;&gt;mu -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>1|county&#45;&gt;mu</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M277.38,-285.96C267.87,-273.38 256.52,-259.21 245.29,-247.13 241.63,-243.21 237.7,-239.24 233.68,-235.37\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"236.17,-232.91 226.49,-228.61 231.38,-238.01 236.17,-232.91\"/>\n",
       "</g>\n",
       "<!-- mu&#45;&gt;log_radon -->\n",
       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>mu&#45;&gt;log_radon</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M192.29,-169.27C192.29,-158.44 192.29,-145.73 192.29,-133.41\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"195.79,-133.78 192.29,-123.78 188.79,-133.78 195.79,-133.78\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.graphs.Digraph at 0x13a3f0e30>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod3.build()\n",
    "mod3.graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1357f68-4ae0-472e-bf6d-e41a901e7f51",
   "metadata": {},
   "source": [
    "### Model fitting and analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fc6b8155-8d2b-4521-aafc-08679e70a954",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [sigma, Intercept, floor, 1|county_sigma, 1|county]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "943673a2685b4fa8b92bd339e3cfadbe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 2 seconds.\n"
     ]
    }
   ],
   "source": [
    "idata3 = mod3.fit(random_seed=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a2bb87e-8d78-4f1c-a315-a57da7f2e00e",
   "metadata": {},
   "source": [
    "The group level parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d9e92f0a-2b85-4992-84bf-78a7435adee1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>0.756</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.725</td>\n",
       "      <td>0.792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>1.461</td>\n",
       "      <td>0.052</td>\n",
       "      <td>1.370</td>\n",
       "      <td>1.567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floor[ground]</th>\n",
       "      <td>-0.694</td>\n",
       "      <td>0.071</td>\n",
       "      <td>-0.823</td>\n",
       "      <td>-0.557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|county_sigma</th>\n",
       "      <td>0.332</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.243</td>\n",
       "      <td>0.415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|county[LAC QUI PARLE]</th>\n",
       "      <td>0.414</td>\n",
       "      <td>0.297</td>\n",
       "      <td>-0.123</td>\n",
       "      <td>0.986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|county[AITKIN]</th>\n",
       "      <td>-0.269</td>\n",
       "      <td>0.255</td>\n",
       "      <td>-0.727</td>\n",
       "      <td>0.237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|county[KOOCHICHING]</th>\n",
       "      <td>-0.375</td>\n",
       "      <td>0.223</td>\n",
       "      <td>-0.802</td>\n",
       "      <td>0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|county[DOUGLAS]</th>\n",
       "      <td>0.171</td>\n",
       "      <td>0.202</td>\n",
       "      <td>-0.212</td>\n",
       "      <td>0.550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|county[HENNEPIN]</th>\n",
       "      <td>-0.100</td>\n",
       "      <td>0.088</td>\n",
       "      <td>-0.262</td>\n",
       "      <td>0.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|county[STEARNS]</th>\n",
       "      <td>0.021</td>\n",
       "      <td>0.144</td>\n",
       "      <td>-0.246</td>\n",
       "      <td>0.293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|county[RAMSEY]</th>\n",
       "      <td>-0.262</td>\n",
       "      <td>0.136</td>\n",
       "      <td>-0.518</td>\n",
       "      <td>-0.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|county[ST LOUIS]</th>\n",
       "      <td>-0.572</td>\n",
       "      <td>0.085</td>\n",
       "      <td>-0.733</td>\n",
       "      <td>-0.417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          mean     sd  hdi_3%  hdi_97%\n",
       "sigma                    0.756  0.018   0.725    0.792\n",
       "Intercept                1.461  0.052   1.370    1.567\n",
       "floor[ground]           -0.694  0.071  -0.823   -0.557\n",
       "1|county_sigma           0.332  0.046   0.243    0.415\n",
       "1|county[LAC QUI PARLE]  0.414  0.297  -0.123    0.986\n",
       "1|county[AITKIN]        -0.269  0.255  -0.727    0.237\n",
       "1|county[KOOCHICHING]   -0.375  0.223  -0.802    0.025\n",
       "1|county[DOUGLAS]        0.171  0.202  -0.212    0.550\n",
       "1|county[HENNEPIN]      -0.100  0.088  -0.262    0.060\n",
       "1|county[STEARNS]        0.021  0.144  -0.246    0.293\n",
       "1|county[RAMSEY]        -0.262  0.136  -0.518   -0.011\n",
       "1|county[ST LOUIS]      -0.572  0.085  -0.733   -0.417"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idata3sel = idata3.sel(county__factor_dim=sel_counties)\n",
    "az.summary(idata3sel, kind=\"stats\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab2ae9aa-9e43-475b-9233-876a9155e6a6",
   "metadata": {},
   "source": [
    "The intercept offsets for each county are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b512a40e-f557-403a-936b-650fbb376f45",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sum( idata3[\"posterior\"][\"1|county\"].stack(sample=(\"chain\",\"draw\")).values.mean(axis=1) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3c662b20-915d-4725-b771-00937e50e7e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# az.plot_forest(idata3, combined=True, figsize=(7,2),\n",
    "#                var_names=[\"Intercept\", \"floor\", \"1|county_sigma\", \"sigma\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "db530317-689e-4c24-a29a-3216810b76ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABRIAAAIkCAYAAABiJdeMAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAewgAAHsIBbtB1PgAAuI9JREFUeJzs3Qm4jPX7x/Hbvu872XcqLZStlCUJFaVNIkpZ2qiQQkokWwtKlPZSpI0iyq70S9mzZIns+77/r8/X/5nmHDNn5qxzjvN+XddcM/PMMzPPPHMUn3Pf3zvN2bNnzxoAAAAAAAAAxCBtTA8CAAAAAAAAgBAkAgAAAAAAAAiJIBEAAAAAAABASASJAAAAAAAAAEIiSAQAAAAAAAAQEkEiAAAAAAAAgJAIEgEAAAAAAACERJAIAAAAAAAAICSCRAAAAAAAAAAhESQCAAAAAAAACIkgEQAAAAAAAEBIBIkAAAAAAAAAQiJIBAAAAAAAABASQSIAAAAAAACAkAgSAQAAAAAAAIREkAgAAAAAAAAgJIJEAAAAAAAAACERJAIAAAAAAAAIKX3oXQAA8bVy5Uo7efJkpA8DAAAAAJDKZMiQwSpXrpwgr0WQCABJQCEiQSIAAAAAICUjSASAJPoN0IXg7NmzdurUKXc7ffr0liZNmkgfUrLAeQmOcxMc5yY4zk1gnJfgODfBcW6C49wExnkJjnMTHOcmeZ+bhPz3KEEiACSBhCojj7QTJ07Y0qVLfZ8pY8aMkT6kZIHzEhznJjjOTXCcm8A4L8FxboLj3ATHuQmM8xIc5yY4zk3qOTcMWwEAAAAAAAAQEkEiAAAAAAAAgJAIEgEAAAAAAACERJAIAAAAAAAAICSCRAAAAAAAAAAhESQCAAAAAAAACIkgEQAAAAAAAEBIBIkAAAAAAAAAQiJIBAAAAAAAABASQSIAAAAAAACAkAgSAQAAAAAAAIREkAgAAAAAAAAgJIJEAAAAAAAAACERJAIAAAAAAAAIiSARAAAAAAAAQEgEiQAAAAAAAABCIkgEAAAAAAAAEBJBIgAAAAAAAICQCBIBAAAAAAAAhESQCAAAAAAAACAkgkQAAAAAAAAAIREkAgAAAAAAAAiJIBEAAAAAAABASOlD7wIgKX399dc2fvx427Bhgx0+fNhty5w5s5UpU8aaNWtmHTp0iPQhAgAAAACAVIggEUhmbr75ZndZsmSJtWrVym17+eWX7cYbb0yw99izZ49lz57dMmbMaKnZoUOH7OzZs5YjR45IHwoAAAAAwM+mvUfszQUbbcrKHbbv6EnLnSWD3VS5oD1cq6SVyJM10oeXatHaDCRTefLkCXg7Pk6cOGHPPvus1a9f37Zt22ap2SuvvGINGjSwZcuWRfpQAAAAAAD/T8UeA2essbIDZ9qgmWttydYDtmnfUXet+9qux7Ufkh5BIpBMpUmTJsFfU/+h/fzzz+3o0aOWN29eS80mT55s+/btS7CQFgAAAAAQfwoLe09dZafPnAsK9U/jgtkzumvRdj2u/ZD0aG0GUrBTp05Z+vTh/zH231frLl4ITp48acePH3et2rHhnYssWbIk0pEBAAAAAKIbOW+9jZ6/IeBjJ0+fsTW7jvjud6tXxrrXK2tFcma2rQeO2dBZ62zYrL/dYwoTxy/aZBnSRa2R61S7lHWpUzqRP0XqRZAIpCDbt2+3RYsW2Zo1a+x///ufFS9e3F544QUbNWqUq7DbsWOH5cuXz2644Qbr2rWr5cqVK8rz06b97z+w6dKlC/geqlb8+OOPbfbs2W7Yi96zdOnS1qZNG2vUqJHb5/Tp024YzF9//WW//vqrbd682caOHWtffPGFjR492q09+P7771vFihV9r6tj0+uuXbvW1q9f715X1YC33367PfTQQ7791HI9btw4W7lype3fv99drrzySrdPpUqV3D6rVq2yP/74w/788093nE8++aRbV3LMmDHuGPTaWbNmde+v49b5iM47F/7nBAAAAAAQ1Y6Dx23n4RMhCzzW7T/pbqfbfsgyZMgQdN8V2w7aiu2HQr6vQsQhzav67itM9O57YaJ/6Oj/+su3HbTEViBbRiuYI5OlNgSJQAqrQFQloUI0BYq6/cwzz1idOnVs5MiRLtx77bXXXIi3ePFimzBhgi8o++ijj2zevHm+EPHOO+/0tTsPGjTIypYt616ze/fu1rFjRzc5Wu3VBw8etKefftoFk4888oi73r17t33wwQcu0NP7KOj76quv7LnnnnOvp4uCPO+Y33zzTfvnn3+sd+/eljNnTrfdu79161bf5/vwww/dvsOHD3ePiT6TjkeB49tvv221atWyAwcOuP8x/f7777Zr1y7buXOnO8YrrrjC3njjDXd8kyZNsu+++84FnZ06dbLHH3/cvd4333xjs2bN8q0Rqe1eqNqtWzerWbNmEn6jAAAAAJC8jZq/wfpPXx2LZ+yM93uqjVmViIFo+/DZf1uwJRJHL9joLomtT6MK1q/xf8UzqQVBIpCCFCtWzF1Urbdw4UJbunSpff/99751/ipXruzCNA0R0WOq2Lv88svdY61bt7Z69erZjBkzrHDhwi5k9KcqPlX9XXXVVXbvvff6tmuisaoeZ86c6aoNFUAWLFjQ+vXrZwsWLLB27drZpk2bXEipY1K1YocOHSxbtmzu+T179rT58+e79/VvI1Y15V133eWeK3p9vU+PHj2sRo0avv1KlSrlwr4nnnjCBgwYYN9++607Rl30/goaVYWoasf8+fP7nle3bl13PoYMGeLCSU29VkVj8+bN3UWBokycODGRvi0AAAAAQFyr/VSBGIi26/Edh2KukkTioKcPSMFUCRh9WEihQoWsTJky7va///4b9mt99tlnrpW5WrVq5z2mgE4t06ouVDjp8aodVbWoakS1UmuIy5dffumuFWYqsLvmmmsCrkWo92rYsKG7/c477/i2Ree1SKulW9WI0dc5VPuyf4joeeCBB1xgqQrJH3/8MexzAQAAAACIHLVSa03EQLQ9VKs1Eg8ViUAKpmrBQLzBI1qnIlxqAZZPPvnEfvrppyiPKYhTG7UCPa/S0F+JEiUCHotaiL1wMxBVV8qJEydci7QokIw+OEUBZtGiRV04qYrH6AIdk6g1u3bt2i4k1RqNAAAAAIDY6Vy7lLWqVjTGffRvz9VrzrU/VyhfIcY1Ekdp2EqI1mO1LWuwiv8aiR5tD9bWLJ1qlbTOSTBspUC2jJYaESQCKZiCskC8Nf8UAIZL6wxK27ZtXWtybASbfOyFd0eOnL8Arr99+/a5sFBefvllu+SSSyyh5M6dO8YwEwAAAAAQnAaKhBoqouKQ0zvOhYdVCmW3jBmDh2xVCudw+4Sa2uwNVAk2tVnK58963tRmvX7VwoGLbhB/BInABSw2QaI3BCUhK/e8/3l46yCGeu+Efn//gPS6665L0NcFAAAAAMRelzql3SWYgTPWWO+pq9xthYYarKLqP7Uz+/8Td0CTStarQfmkOGT4YY1EIBUKFDBWqVLFXWswSkKpWvVcGfovv/xihw4dCrqf2qa9dR0T8v1FA2C0RqN3LPEJWwEAAAAAiatn/XIuJEyX9lwHnv7JpsEq3j/dtF2Paz8kPYJE4AIWfT1BrxVaA0u8VmLP3Xff7YaXrF692qZOnZog73/DDTe4dQ2PHTtmI0eOjHFfb1L0pEmT7J9//onV+0T/LJ7Jkye7gTCa9hzM3r17Y/VeAAAAAIDEo3+3qtJwXa/61qtBOatWNKeVyJ3FXev+ue3lgy71hcRFkAgkU/4B1+7du6M85q05GGrtweghmUI9tRurOvCpp56yb7/91t5//303rblSpUrWt29fFyb27NnTJkyY4Na58Pz+++/2/PPPR3k9r8rw6NGjAd9fQ1BGjBjhrjWVefjw4VEqE9V27FUg3nPPPdaiRQv3mbROoyoJ/YPC7777zjfZObohQ4a46dBecHrmzBl3XwNkJk6cGHB9xMKFC7vr7t2721dffeWGzGzdujXG8wkAAAAASBol8mS1AU0q2+Ju9WzDsw3dte5rOyKHNRKBZEYB2LvvvmsbN/43xapXr1729ttvW/PmzV21oKoGZcaMGXb77be7AK5169bnvdbo0aPtxx9/dKFhjRo13FAUDTN56aWX7IcffrA///zTOnXq5Jt6fMcdd9jFF1/swkU9V8GfAsZcuXK560ceecTtp5Bv2LBhvrUPdTyqPtR+r7zyipUqVcp3DFdffbUL6saOHevCwI8++sjKlSvn9r3ooot8x63fJg0aNMgaNGhgn3/+uXXr1s21PJcvX95NhK5Vq5avajE6TXTWseqzabJ0wYIF7ZZbbnGfMxjt++yzz7q26zVr1rjj8MJFAAAAAABwPoJEIJlRWKhLMO3btw/5Gh988IHvtqr0/Eu+b7rpJncJRmslKtCLSc2aNV3FYriKFy9+XjVjMI0aNXKX2LjvvvusZcuWsXqOPoNCVgAAAAAAEB6CROACly5dukgfAgAAAAAAuACwRiIAAAAAAACAkAgSAQAAAAAAAIREkAggxfKmRYeaXg0AAAAAAOKPIBFAiqPJz5pW7Q1L0cRmTZyePHlypA8NAAAAAIALFsNWAKQ499xzj7v4T6MGAAAAAACJiyARQIpDgAgAAAAAQNKjtRkAAAAAAABASASJAAAAAAAAAEIiSAQAAAAAAAAQEkEiAAAAAAAAgJAIEgEAAAAAAACERJAIAAAAAAAAICSCRAAAAAAAAAAhESQCAAAAAAAACIkgEQAAAAAAAEBIBIkAAAAAAAAAQiJIBAAAAAAAABASQSIAAAAAAACAkAgSAQAAAAAAAIREkAgAAAAAAAAgJIJEAAAAAAAAACERJAIAAAAAAAAIiSARAAAAAAAAQEgEiQAAAAAAAABCIkgEAAAAAAAAEBJBIgAAAAAAAICQCBIBAAAAAAAAhESQCAAAAAAAACAkgkQAAAAAAAAAIREkAgAAAAAAAAgpfehdcKHYsGGDvf3221G2Va5c2e69996IHRMQCc8//7wtX77cDhw4YEeOHHHb3nnnHStXrlykDw0AAAAAgGQryYLEEydO2J49e6xw4cJhP6dixYo2cOBAa9myZaIeW2qxfft2++KLLyx9+vQ2ePBg913E5vsALhR9+/Z11/pvUq1atdztdOnSRfioAAAAAABI5UHitm3b7LPPPrMJEyZYixYt7Mknn0zst0QI+fPnt6ZNm0bZNmjQIJs/f76tW7fOTp065bblyZPHihYtak8//bTVrFkzzu83a9Ys69Wrl/3000+WKVOmWD9fYc8333xj//vf/9zPk4LQ06dP25kzZ+zw4cNWqFAhq1q1ql1yySUuFMqZM2fYr/3PP//Y5MmT7ffff3ef++TJk3bw4EFXpVamTBmrXr26+7mNHrjqPOm8KJzduXOnL4hSRdsVV1xh/fr1O++9Hn/8cVu7dq2tWbPG3S9VqpQVLFjQRo0aZffff7/7nFu2bPHt7z3+wQcfBD3+8ePH21dffeWO5/jx477t+t70/e3fv9/27t1rBQoUcMfVoUOHkFV3rVq1sptuuskdU0wCnQPJly+fO259N7t27bLMmTNb2bJl7bbbbrNbb73V0qRJ4/bTeY7r59b307VrV/e+q1evjvKYvgf9bJw9e9a3TT93V111lY0dOzbKvnnz5rWsWbO618uQIUOMnxcAAAAAgNQuUYLEQ4cO2cyZM+3bb7+1rVu3uqBBgQZiT+evSJEiCfqagSqvevbs6a7HjBljQ4cOdbenTJnigpb4evfdd2337t0usLvzzjvDft6xY8ds+PDhLoRWCPXYY4+5QMqfAsVff/3VBgwY4Nq2f/jhh7CCRIVcL730ks2bN8+6dOlir7/+umXPnt33uMKtqVOn2ptvvmlvvPGG3X333S4EVygmOo6JEye64FEhq37mO3fu7MKtYEaMGOGu+/TpYzt27HCv7VGlqIKvZs2aubDxuuuus7feeivk52jXrp276Lt64okn3LbvvvsuSli4adMme+6552zSpEnuM73//vt26aWXBny93377zZYsWeICwDZt2rjQNphA50DH8PDDD/v2UUCnsPO1116zRYsW2fr1661bt27usRw5crjPrdBPn1vBZLifW+GfWpGlefPmLkzU9ZAhQ9w2nUt9xzqmzZs323vvvecC9ECyZcvma28GAAAp17bDp2zi2iP2v5/m2f5jpy13lgx2U+WC9nCtklYiT9ZIHx4AABeERBm2olDj66+/dgGHKsnUoozY2bhxoz300EN2zz33JOn7qprMkxAh4qpVq2zBggW+QNG/SiwmqjS7/fbb7dNPP7XRo0fbs88+e16I6IWiqkIcOXKkqxpUNVsoCskUTC5evNg+//xzV4HnHyJ6n71169au2q9KlSquOk4/zwqn/KmKLVeuXOedu5go0MqdO/d521WppypC8a7D5f9dqULTX4kSJdz50XsePXrU3Q5GoZ/8+++/LnQMh86BF95658I/8FPAescdd7j7Cv8U0vpLmzat7/hj+7nFO5f+oafOpb5T/UyoqvTaa68NGiTGFJYCAIDkT3+/HPzz33brNzts/IpDtnTbIdu076gt2XrABs1ca2UHzrSBM9aE/fdQAACQxEGiQgO1ENauXTsxXj7VBIk///xzwMApMXltpwlFwVH9+vVd4KdqtBkzZoR8jlp027dv71qAFSCG01ZdsmTJ8wK0QNS+3LFjR3d+VSGolt+YKOBSRaICMgWP/fv3t8Q8x3E9//7PC9Siq1BNrb1eO3cgOiezZ892VYHiVfzF9v0DUZAnql5UhWBsnx9f+m/RjTfeGPAxBZkAACDlUljYZ9oaO/3/OaH+WlEwe0Z3LafPnLXeU1e5/QAAQPxQihMLan9UhVVcHleAFZvKJy8M8lpp40K/dVVwoxAvEoMktJ6hqtpUlaqw5scff3ThVMOGDWN8ntqr1aqqCkRVJcZmEm8oH330kZvWW69ePatQoUJYr6uAUq3NakVWe7Zad0uXLm3JVbBQTq3iEqxCWN/NDTfcYA888IALsVesWOGqSb1hJPEdtuT9XCv0TWrFihVL8vcEACCpjJy33kbP32Cp0cnTZ2zNrv+WKOlWr4x1r1fWiuTMbFsPHLOhs9bZsFl/u8cUJo5ftMkypEtdv0TUvwmOHTu3lnbmGXN9f1fsVLuUdamTfP9OCwBIni6oIFHBh9Zs05pwWmdOwZ5CoO7du7vgyH8/rRP3119/uf+Rar26iy66yG6++WZXTekFfmrBXLp0qdtPa+llyZIlyrp2qqxauXKlez1VcmmYRe/evX0tvd5zFy5c6NpiVZmndfk01OTAgQMuUFFb7X333XdeVZT3P/joAaBabLWen7fmpCoWixcv7taA01pvel+t+edVfamdU2vOXXzxxZbUxo0b5yrB1G587733uiBRA1P+/PNPq1atWsDnaM1DhX3SqFGjWFWqVa5cOeQ+Gvwjsa2WveWWW3zfvVr3dY5TEoW6v/zyiwvyVJEZqJX8yy+/dOe+UqVKrh1Y6yXqO0yIIFFLHYjWXYze/pzY9LP34YcfJul7AkBytePgcdt5+Nwvd5Iz/SJ03f6T7na67YcYiBXivKzYdtBWbD9kqZ1CxCHNq/ruK0z07nthon/omCodODdU0fu5Wb7toKVm/LcmqgLZMlrBHLEfjgkgdbkggkQFfhq2oSBJQ0O8/wFouMNTTz0VZV07VV2pVVVTdTWpWBTqDRw40FW0qXpOQzvUCqpJsgq9NMBDwaQmzvpTSKjqNg2MUBDjH1YqENMwCT1XQaUGWCiY0UAIte1qSrAGieh9FfJ4w040yVbr1KkiTFSZ560vp+pEBYaaqKvKMQ2SePTRR91afh6FQGof1vqKOn4dW3yqGuPznWj9QW89PoVRqgDU51E4peEbgeh8asqwhFsxGC59DxroIQqOY0MTnNXmrM/199/n/iKanKkCMGPGjO62fobVIq7JxYMHDw4YuGr9Sq0FqcnXXuCnn9c5c+a47yyu34V+nocNG+Z+nrXepzdoJamoClMt2wCAc0bN32D9p0eddp/87Yz0ASRTnBd/+t2zKhED0fbhs/82lkiMavSCje4CD3+m+jSqYP0aM98AwAUeJKrqUAGFqt7UkumvRo0aLtzTwAX5/vvv7eWXX3ZhhqoPPRoUoUBPa/gp4NNUXYUfClV0UTuAgsTo1XENGjRwFwV2qrjyf7xq1aruogovBVh6bVVBevsotNFwjscff9xVyWkisCohFT4+8sgjrhWzV69erorRCzw9ep6qrFQhp9DQP0gUBYyaZKwQLxIhouiz6jPUqVPHt03HrHM7ffp0t06fKikDTan2JPT6kJoe7ompRT0YVbcqSPSCzuRME9NVYagQXaF406ZN7a677gp4ThWka6jNiy++6NumalBNC9f3ofA9+s9gTBQg6+dS50mvrVZ2TZIONCwnISn09EJ3LSWgEFN/NhXqAwCAC7+SShWIgWi7Ht9xKPlX4wIAkNyl+AVCPvnkExfSKfgIpEWLFr4AQ+GgBFujzwsiFXoogAh3GEM46w8qcIweRHrttQpDYxtOqR1aFWdqm1ZFoz9NGlYrc6SG3SjIVICqY/Sn8FZtrWfOnHEVcIH4hz6aMJyQFC55vNbw2FBFX1xDyKTWsmVL1wY/adIkF+pqXcdgwazafhWmq8rV/2da60J6oaR/CBuK/nzpz+Qff/zhgkQFyIkdInp/1idMmOAu+twKrBUupoTvCwAAxI9a9rUmYiDanhJa+gEASAlSfEWi1wIcbGLvZZdd5q5VAee1OHoVitEpeFPYpwpEtYMm5IAGVRpGp0qxQCFXOFSVqHX7VP2l4EQtzp6PP/7YnnvuOYtkuKvw1b/qU7TG5G233eYq3BT0qPIyT548UfYpWrSo73aw6cJx5f+9q123SZMmsXr+rl273HX58uXjdRwKS5PLpGCFtQoa1W4ffRiQqvtU1aop2tpHywSEQ9+plgfQ+p86Z1pPUsGyF8QmJf05if4zBgCpWefapaxVtf/+X5uc1y1bveZcC3aF8hVS/bploc7LKA1bSeUtqmpb1mAV/zUSPdpOW/P5OtUqaZ1T+bAV/lsTlSp3AeCCDxLVNhxO9ZoXAnn7ajBJdKpcUmWWqtX0P5WEFGhoiH8lo6r0Ykvr2ClIVGu0Ks5UoaihMKr6q1mzpkVqbT6t46iW6rZt2573uEIp/Q9a34ECz+hDS9TyrdBVbamqJrv//vsT7NjUquu1686dO9cee+yxsJ+rFmGvKu/6668P+N2G20KrdfsUqiYHCqFVQarp2hqGE53+PGj9T/2MderUKUr4HZMSJUq4tUj1/WnokFrataxAJMycOTMi7wsAyZEW0U8JC+nr7xOnd5z7B32VQtl96/6mdsHOS5XCOdz91D612RuoEmxqs5TPnzV1T23OnOm/pZYK57Cqhc8vdkhN+G8NAKTCINGrcgo1UMF/UqzaLjXNOBDvfx4KQpL6f/CxVbFiRRcYqr1ZazTefvvtblBL586dLVIUbCokVKVooCpM0cAPDVxRlZrayf0r1XT+tZafKto03VoTsRUuJhRv+rKG32jCtYbThGPWrFkuXL7qqqvs0ksvjfKY1zqrNt5wKJhLilbfcP7ipO9BU5z9K1r9aU1IBacKdvXdxibY1RqlPXr0cGsvTp482Q150fTySNH3owA3tf+mGQBw4elSp7S7pFYDZ6yx3lNXudsKDTVYRZVVamf2/yv2gCaVrFeD+HWWpET6O59+sSta/52wDAAQHyn+13EaaBJO1ZGGsajNUVTpFoiq5bRWoSb6VqtWzbfd+61dsKpHrXEYqYDRW4dQaw4qGNPx161bN9GOx/+4VAUZ/S8pCgC1tl6wEFFUqagwZ/fu3W4gSHSqfCtZsqR7D02zVsVcQlEQ5v0cqP1bxxyKKg31ubTGYKCqOq8dW+c/FFWeLl682DcdOZI00VsBoSpbg9Gkaq09KKo0jW2lrl5bU8a9AHnBggXxOmZVkqrNOi6efvppXwUzAAC4cPSsX87631De0v1/A5D++qzBKt5fo9OlTeNCRO0HAABSQJCYmFNT77zzThf0/e9///OtlxiI1qTzhkeoTTPQsI1p06a5Y9W0ZP817Lw11tasWXPeczTl+aeffor354je2uyFl6GGsKhaTNWTmirdtWtXt9ZdUlRGrly50rX7+ps4caILB0NVnWk9Sy9cGjt27HnrQ6rtXJVymur8119/WYcOHc4bKBNMqLUmFQYOHTrUBZmqSlTLbUw/nzofCsAUQGm9QP81HD3eWotqDVYVZUzU7qsgMynC3ph4oa/WMQy1hqDCV/15UEu4Kl9j64UXXnBBvs6zppRv2rQpzj93MVW6hrJs2TIrUKBAnJ4LAACSL/29+enrytjk5gXt/irZ7dIiOaxE7ixWrWhO69WgnK3rVd9VIgZaaggAACSzIFEBgbc+odokE5qGqWgIhP5i8MQTT9g333wTJRhSlZhXKaYKumuuucb27dvn2jn926FVpaiASaFH9KnOmgit9tV169a5CjlVlClAfPXVV10lpLdmXqDKRG9bqKrF6OfGGwyiCqzhw4e7SdJqLY1OAY9agb0wsnHjxhYf4bTnqipNx+Pf/q3KttGjR1v16tWDto378waxaKCK1umLTiGiBrKoXVutGE2bNnXfT6AwV+dWAZMCxw0bNoR871q1arkKTg3TUUVk69at7ZdffjkvzP3111/dz4mCL7Xm6rMFcuutt7oqS/3cPfjgg/bkk0+6/fV8DXX57bff3GRkhd6zZ892rdWB/iKrPyv62Yy+pmc4/APncP6c6Xj+/fffsH5eVB3qVeiOGjXqvMpchbf6/kVBcnRaV1F/VtRWrM+nP4fRf87CmaKt56rFXD8b/sKpkly0aJGrOKaVBwCAC1fhbOmtc7Wc9usjtW3Dsw1tcbd6NqBJZSuR59wyNAAAIP7SnI3L4nxhUPWWKvUUamzZssW3XRNvFSi89NJLIaffag3AgQMHWsuWLUO+n8IatV4q4NNHqlChgnsfrYGngMkbEqHQQ1OFFSApSFRIovUTtZ8COd0PFkQMGTLEVeIpBNLrq1JLlXUaGKJqNA0Y0TErTFq+fLkLjXQtqqIqU6aMPfPMM75J0t5nlIIFC7pBIAq4vEEwr7zyihtIoqBE56pv375RnusfIl177bUuUPVanQNRWKbHFaAFagVXOPj888/7Qh3/9m6FZDoOBUBa40/31SqqCshu3bq5llUvwNLn1PGosjO6P/74wwYMGOCq2/Q63tCZcuXK2ZgxYwJO1NbPj45NwZwqLxX4qYpO++q5mjZ8xRVXuFBM5zBcCpY0ZEThlL5XBWR6XQ0Y0c+L1jFUkBnsZyI6VTiqKlMTv3XMqtjU+o+qgrv44ovd8SmUjj6xWSGczqNCOP8/K6ri08/FBx98EPQ9tSbmV199ZX///bcb4uJVXao9v3v37m4SuT+tp6k/ewo49edEP/vaVz9rgdZt7N27t/sZ1v5eQK/Po0rGZs2auTUQNYTGaxnW96GfVYW1Ct396TvUz4r3Gs8++6zdeOONbpseE51//4Baf171vehnXBedz+nTp7uqVv3SQOG+/szosyvsV7Wn/vzoZ0LnWc/Xz5l+NvU5VXUcXf369d15nzFjhtsHMWOdpcA4L8FxboLj3ATHuQmM8xIc5yY4zk1wnJvAOC/BcW6C49yknnOTaEEiYk+hhwKQ2FJIoqq477//3jf4I6YgUe25gdqxvfUC1fYbm9YPVQQqQNIfhpTaMqKQTKGt2n31GTRURS3LqmC9kIZz6GdM37M+UyQ/l1f9qbBPIaB+7uPys58QCBJT9/8EEwrnJTjOTXCcm+A4N4FxXoLj3ATHuQmOcxMY5yU4zk1wnJvUc25S/NTmC0lcgxRVYipIjClEDGcdwbj+MIf7vsnV5s2bXfWcqtxUOaj1NrUWoC6q7rvhhhtc+7r+wOu+glvtmxJFMrDz51+VqUreSApn4A4AAAAAACBITPG0bpym7waafhyMWkTVduu1UKd2ClZfe+01N6HYq07UOpiqUFPLutZw9NZxVGiq0FHrHSLlU1t2qIFGAAAAAADgHILEFEjDY7S2oiqpNHVYa+9pLb1QtL6cV0mnNe60DqAGiGitw9RMaxH6U5u2zosuWgNQ6zKqSlGDYTSYJTbrMCJ50p8brauqtnytqygXUgs7AAAAAACJgSAxBercubMbFKK15RQOfvvtt2E9T4NaNAAGsaNBMLrgwtG/f/9IHwIAAAAAAClO1PGxSPY02deb9Ks1+95//31fSy4AAAAAAACQWKhITGHUhjlz5sxIHwYAAAAAAABSGSoSAQAAAAAAAIREkAgAAAAAAAAgJIJEAAAAAAAAACERJAIAAAAAAAAIiSARAAAAAAAAQEhMbQYi5LnnnrMzZ8747mfOnNltQ+IbNWqU/fzzz3bkyBE7cOCA2/bSSy9Z3bp1I31oAAAAAAAkWwSJQIRMmDDBXXfu3Nlq1apluXPnjvQhpRo657qcPHnSnfuDBw9G+pAAAAAAAEj2CBKBCGvRooWVKFEi7P0VfjVo0MCeffZZu+GGG2Lcd+HChTZ48GDbsmWL7du3z21Lnz69lStXzgVoDz/8sF199dUuxCxUqJBlzJjR0qZNa4cOHbJ169a5/atWreqe49Fr6fnPP/+8de/e3bZv3247d+50j6VLl849dsUVV1i/fv2iHMvZs2etUqVK7nbevHmtSJEi9s4779jEiRPt22+/de93/Phx93j27NmtZMmS1q5dO3du9FqrV6+206dPu8f13nfddVeMn71Xr172yy+/uOPVZypdurTdeeed1rZtW98+GTJkcMehIFG3AQAAAABAcASJQIQp5IqNKVOmuPBOIVyoILFmzZo2adIk2717t9WuXdtt69atm3Xo0MHdPnHihAvQZs+ebZkyZfI9TwHcfffd526PGzfO8uTJ43vs+++/t6lTp1qpUqVcCKhgU++j8FFVfl27dj3vONTC3adPH/dePXr0sHvuuceFjqJj0UWf64knnnDbxo8fb5dcconv+ZMnT7ZZs2ZZx44d3f0XX3zRKlSo4ALLYAYOHOiub7nlFqtevXrQtvGsWbPGeA4BpF6b9h6xNxdstCkrd9i+oyctd5YMdlPlgvZwrZJWIg//7QAAAEDqw7AVIIVRyCaLFy+233//PaznqALQ4x8KehV5/iFidNEfK1CggFvP0f81cubM6W7ny5fvvOcraHzqqadsxowZ9t5771mbNm18IWI4x+gpW7asbz+9pgLLzZs3Wyg6tpjaxv2rLQHAq6AeOGONlR040wbNXGtLth6wTfuOumvd13Y9rv0AAACA1IQgEUhBFixYYFu3brUrr7zSVy0YjjRp0gTdrgrBmEQP/RToqR07nNdXq/Ijjzxiq1atss8//9x33LE5xuiGDRvmwkRVWXbq1MlVQiZlRSiAC5/Cwt5TV9npM+eCQv3nqWD2jO5atF2Paz8AAAAgNaEUB0hBFBxqnT+1/f7vf/+zmTNn2oYNG1ybcVw1bNgwVvuruk+tzKEcPnzYtTorqPv0008tR44clhCKFy9ub775pmu91rqJjz/+uL311lsBqxwBJJ6R89bb6PkbQu6nqr1jx86tf5p5xtywf2kQKSdPn7E1u4747nerV8a61ytrRXJmtq0HjtnQWets2Ky/3WMKE8cv2mQZ0sXtFxLJ9dx0ql3KutQpHenDAAAAQDJEkAikECtXrnTDUwYMGODai4sVK+YGibz77rtu+EhysmfPHjfIResYalBKQrcPV6tWzYYOHeqqHefMmWMvvPDCecNdcGHYcfC47Tx8wpI7tduv23/S3U63/VCqGN6zYttBW7E9lhXBB05ZSqIQcUjzqr77ChO9+16Y6B86xksyOjf6bpdvi+w0+9T4ZyopzkuBbBmtYI7gy5kAAACEQpAIpBCqwmvcuLGbriytW7d2E5k1iOSxxx6LssZgJG3cuNEeeughu+222+zBBx9MtPdRJaUmMytY/eSTT9x05/bt2yfa+yEyRs3fYP2nr7aU5dwUc6RsKg5UJWIg2j589t92oS6ROHrBRndJPvgzlVDnpU+jCtavccVEORoAAJA6sDgYkAKsX7/epk2bZm3btvVtu/322y1Llix27Ngx++ijjyw5mDt3rt1xxx124MABu/766xP9/dTe3K5dO3f7lVdececIABKCKrdUgRiItutxAAAAILUhSARSgLFjx7p23ksvvdS3LVeuXHbzzTe72x9//LELFCMte/bsbqKzBqHcfffd9ssvvyT6e2pYjCo1z5w546ZD//nnn4n+ngAufGqp15qIgWh7Smi5BwAAABIarc1AMrdt2zb76quvXBtzdG3atLHPPvvMrUk4adIku+eeeyySLrvsMnviiSdcS7MGoXTo0MFeeuklX+CZGDTMRdWIO3bssMWLF7u1GXVO1OqMlK9z7VLWqlpRSwnrlq1ec64Fu0L5CqliPbdRGraSrNpfE5baljVYxX+NRI+2X6htzdKpVknrHOFhK6nxz1RSnBcqaQEAQHwRJALJ3DvvvOMmEut6/PjxAaco79u3z9577z276667XLAWSYULF3at1l26dLFff/3VVQlqKEynTp0S7T0zZcpko0ePdp9fU6wVZGrdxOSybiTiTkMBUsJggBMnTtjpHef+QV+lUHbLmPHC/8d6lcI53GeN1WTizJmSzWTicKY2ewNVgk1tlvL5sybM1OZkdG703VYtnCOix5Aa/0yFg/MCAAAijSARSMbUIjxhwgTr06ePG14SyJo1a6x58+YuQPvxxx/thhtusEjLmTOnjRs3zrUdT5kyxUaMGGH//vuv9e3bN8EnOHvy5Mljb7/9tt15553uXHTs2NGFqwASR5c6pd0lnOBj6dKl7vYll1ySIoKPgTPWWO+pq9xthYYarKJKLrUz+1ciDmhSyXo1KB/n90mJ5wYAAACpG2skAsmYqhBVcRhTa3D58uXtmmuucbcV3iUX+gfxsGHDfMNQFIiq7fjw4cOJ9p5qZx41apR7b/3jvGvXru4f6gAQGz3rl3MhYbq05yoEFR7uOPRfiKjtelz7AQAAAKkJQSKQTGndQw1Ruf/++0OugdS+fXt3/ccff9jChQstuVCbXq9evdxFt+fMmWOtW7e27du3J9p7Xn755TZgwAB3e/78+e6cAECs/9vVoLyt61XfejUoZ9WK5rQSubO4a90/t718smlFBgAAAJIKQSKQTI0cOdKOHDliN954Y8h9a9WqZQUKFHC31UZ8+vTp80JJz65du8JqqQ50O5BTp07ZwYMHY9xXVYlDhgxxgejKlSvtjjvucNf+9u7dG+N7ah1I8d4rJqrgfOSRR0LuBwAxKZEnqw1oUtkWd6tnG55t6K51X9sBAACA1Ig1EoFk5uuvv3aDQ/7++9xi/i1atLAiRYq4QSs5cpy/+L3WAty6davt3LnT3dfk4oYNG7rtpUuXdhONNezE8+qrr9p3333nwseePXue10r9zTff+N7be/+LLrrIDU2pWbOmb7vWIXzyySdddeGBAwfcNrUVT58+3VUF9uvXL8prN2vWzK1jqHZjTaJWmNiyZUsrWbLkee+pCkttVzWm1g3TJOhNmzb5JlUXL17cXnvtNXcdjN5H5+TTTz+NxdkHAAAAAADBECQCEaapnf5UgdioUSNXvRfOYJIxY8bE+PjEiRPDPhYFeF6bdCilSpWyL774wmKjTp06LugM9L4x+fLLL323z5w546ogQ7V7y/PPP29t27a1kydPxuo4AQAAAADA+QgSgQiL3obM1M6YpU2bNlbnqEyZMjE+TsgIAAAAAEB4WCMRiLDNmzdH+hBSrUOHDnH+AQAAAAAIExWJQIQULlzYtTVrLcG8efNa1qxZ3TqISHxvvPGGTZ061Y4ePeoqHAsVKmSZMmWK9GEBAAAAAJCsESQCETJr1qxIH0KqpUEsugAAAAAAgPDR2gwAAAAAAAAgJIJEAAAAAAAAACERJAIAAAAAAAAIiSARAAAAAAAAQEgEiQAAAAAAAABCIkgEAAAAAAAAEBJBIgAAAAAAAICQ0ofeBRea5557zs6cOeO7nzlzZrcNSC0+//xzdzl27Jjt27fPbXvkkUesVatWkT40AAAAAACSLYLEVGjChAnuunPnzlarVi3LnTt3pA8JSFIKDL3QsFmzZrZmzZoo4ToAAAAAADgfQWIq1qJFCytRooTv/qJFi2zQoEG2efNmX5VWpkyZrEyZMtaoUSPr0qVLlOf/888/1rBhQ3e7UKFCVqxYMfvkk08Cvtf8+fNt2rRptmrVKkuTJo0dP37cDh48aBkyZLBLL73U6tWr515L90NZt26dff311/b777/byZMn7ezZs3bgwAH3mlWqVHHhqMKhXLlyhXUeFixYYD/99JMtXbrU0qZN6wIlXfR6Ur58ebvkkkvs8ssvd9dt27a1bdu22YYNG3yvUapUKcuZM6ercotOlW7r1693YZWnaNGili9fPhszZoy76Pzoc506dco9nidPHrfP008/bTVr1jzvNU+cOGE//PCDO/a1a9da+vTp3THrXBw+fNiyZ89ulSpVcuf2qquucq/bvXt32759u+3cudO9Rrp06axcuXJ2xRVXWL9+/aK8/p49e6xjx47uesuWLb7t2l8/D6+//rq1adPGduzYEeU86Gcgb9689u6779rYsWPtm2++8T1fn3fixIlWpEiRoN+F9u3atatt2rTJDh065D6HfkZfe+01K168+Hn767Pq5+rXX3+1FStWuM+UMWNGO336tKs21LFUrVrVfcbq1asHDM0vuugi992E87MHAAAAAEBqRpCYiik081ejRg0X9Ozevdtq167ttvXv399uvfXW856rkO2FF15wQY+umzRp4gLC6BQcPvPMM5YlSxYXTPXp08f3vgq9Vq5caR9//LF169bNChQo4FqsFVoGsnfvXnv++edd4KdqyoceesiyZs3qe3zr1q325Zdf2rBhw2zw4MH28MMPu/dUuBTIb7/95gvQ9HqPP/54lNcThW4ffvihvfTSS9a0aVMbOnSovffee+6xBx54wObMmeOCy/Hjxwc9zwrdRJ/9s88+c8GYQkDvPPTs2dNdv/322zZkyBB3e8qUKS4EC1ZROnz4cBdqPvjggy7gVJDoT8HYiBEjrFevXu77ueOOO9x3q+BVwaRCOgV2+tyB6L2/+OILF04qlFXI2bJlSxs4cKBvnw8++CDKeahQoYILeL2fgyeeeMJddF6nTp3qfq4URitsVkAdiIJIfYcKBRV061xffPHF5+2n49L7v/HGG1a4cGEX7vbu3dv9PPr7+++/7ccff3TnQfsp2Iwu+ncOAAAAAAACI0jEefwDrEBhlkIoBVAK7hRqlS1bNuDrKDxSRd1tt93mAsLogZ4CJ1UQvvjii3bdddfZY4895sItPadDhw5R9lWQ1a5dO1elp0AsUGWZKt10XAr8Wrdu7YK0ZcuW2auvvnpe0KaAStWX9evXd8FjsGBL4abCMFXKXX311VEeU4WdVz0Yjvz58/v2jx7ihnPeVVX41FNPuZBR1YUKSYNRFaU+9w033BClolFVd6qc1HcYznHrOHUsOv/e543O267vJFCYrJ8PPaaq0eXLl7vgVOc80L4eHaP3moFC7EcffdR+/vlnu+uuu1yAqCrEQFQ9qfOkz/rnn38G3Cf6zwaAyNi094i9uWCjTVm5w/YdPWm5s2SwmyoXtIdrlbQSeQj8AQAAgOSAqc04T0wBz/79++3+++93raMxhYiq9lPopccV9ASrCvSorVlVY/LKK6+4Vl+P2msVLCr8UsVeqDUdS5Ys6dpq9Z6qRhs5cmSUx7/66itXYajKQFUYBgsRo7+m2rfDPU+BhNo/1OOqnlSIePPNN8cYIvoHZKrwi/Rxi6oVve9Xn0GtynGl6lWFiHXr1nXnJFiIGL3qUOcikEChLoCko+r0gTPWWNmBM23QzLW2ZOsB27TvqLvWfW3X49oPAAAAQGTxL2iEbdeuXXbvvfe69QC1Bl6wijb9Y09VhmqjVRVhuGvP3XnnnW69Oj1fFWueUaNGuerHG2+8MeA6eYFofUBVJsq4ceNcW7ToWiGiqKpNE6vDoTbbyy67zCJl4cKFbv1FhaNPPvlk2M9TW3k4QWlSuO+++9zPg/edqg06tiZPnuzCYYWXarsPNxRVu3z79u1j/X4AEp/Cwt5TV9npM+eCQv2xLpg9o7sWbdfj2g8AAABAZNHTh7BosIrWwtN6eRoeEpMlS5a4tQ/l+uuvD/s9FDiq2k4hk9ZB/Pfff11r8aRJk9zjakOODa3pp7BKrbCzZs1yaz3qvgbJ6L1ic2wKOCPJW49Qw1OiVxjGpHLlypac9OjRw4XCWiNSIada1TUEJbbnQQNkglUYBhJO1SKQkoyct95Gz/9v0FFypl8OHTt2bnhV5hlzo/wC4OTpM7Zm1xHf/W71ylj3emWtSM7MtvXAMRs6a50Nm/W3e0xh4vhFmyxDupTzO9BOtUtZlzqlI30YAAAAQIIhSERICgZVvaeWUgV9ofzyyy/uOlu2bGFPTvZocIj/oBYNO9FkXlHoFBuqINQ/WPWPWL2WzJ071xcMpqQhG/PmzfO1CKdkaiNW67qmPS9evNgNX/n000+tdOnQ/9BWNanWvIz+c4Kkt+fYadt7/Iyl236Iadf/TxXY6/afdLeT4rys2HbQVmw/ZCnOgXOT6QNRiDikeVXffYWJ3n0vTPQPHVMCfU/Ltx1MNj83KUm456ZAtoxWMEfyqLwHAABIDQgSESOtJ6hWUk28jT5sJJjt27e7a01qjq2CBQv6bitA1D8kPOG2IXv0/jly5HBDPrwwctu2be461DqLsaH1HDUVORTvvWNLAdrRo0djNdglHFpHUhOSQ1m7NmHbCdVqPXr0aDcoZcOGDW69R02zDjalOvrPlcSmKhMJ74s1h+3tZQqxdkb6UJIpzktsqUhRlYiBaPvw2X9bSlwicfSCje4SHn5u4nJu+jSqYP0aV0zSowEAAEjNCBIRI03PVRWAAh+FZWPGjLGKFWP+C7sGsXhDUnQ71KCVYC2oCs0UAnpUnRhsuEsw3vqAXgDnHZsXzCWE2rVr2/Dhw0Pu9/rrr9sbb7wR69c/c+aM7/aRIwlXjaNW9bvvvjvkfm3atLFff/3VEpK+j7ffftuti7lp0yY3bVuTtGNaz9H/syfk9wcg8lRVpgrEQLRdj+84dCLJjwsAAABAVClnoSFEhNYR/Pjjj11Foirq7rnnHluwYEGMz/GqxRSAbdmyJVbvp/DRo3ZX/8ozrdMYG2pp9oaslClTxl0XKVLEXWv9Rf+ALjlT9aRX3Rnbc5CcaWr2m2++6SpN1easKd8xTWXVepmeUD9XCr5vv/12t6bntdde6y4KfDXlGUDys/PwCbcmYiDarscBAAAARB4ViQhJ6/Kp9fTBBx+01atXu+sXXnjBTTIOpEaNGr7bWpNQ4WO41q1b566rVKnihmkoPFKIpgo0rRPYqlWrsF9LYdKpU6csffr0Vq9ePbetZs2a7phU6fjHH3/YFVdcYcmdKjp1TmfPnu0qA0+cOHHBDA+pVq2aDR061A3w0QCWgQMHuiEsgWhit34eVJn6888/27PPPuvWXAxEk8W/+OILd1vDe1599VW3xmbfvn0T9fOkFreXz2YNSmSxCuUrsKbb/9MyDKvXrHa3k+K8jNKwlbBbZpM//Q5Bg1X810j0aHtKbGuWTrVKWucYhq0k9c9NShLuuVG1KgAAAJIOQSLCoorEjz76yA3HUJjVs2dPVxXWtWvXgENONMxk8+bNLoBU+6z/lM6YzJw5012r1VUUmDVq1MhNW9ZjWicv3PXxvNfS+3utzbfddpsLltQmqzUCdTslUHuxgkSt9agK0Xbt2tmFomHDhtarVy8bMGCAa2/Onz+/3XTTTQH3VZWh1lfUz95PP/1kDRo0CPn63s+LKjvD/TlEzPJmTucuVQplv2BC7fhSwH96x7mgIynOS5XCOdz7pLipzZkzBZ3a7A1UCTa1Wcrnz5qipjbre6paOEey+blJSTg3AAAAyRNBImK1XuK4ceOsR48eNmXKFLfmnwKd/v37R6kUUAWgKsy0n6YlaypvOGvxaTq0wjK1oyo89Ci8VLXa8ePHbdCgQWGtR3jo0CEXSmlNxccee8y3XQM9dFyqTJsxY4YbNhKssjI5UWtu06ZN7bvvvnPnvVatWiHXqkxJ7rvvPrdW4gcffOAqFIOtBal1HTX8Z82aNa5NWaF1vnz5YnxtL7SIy/AfILnqUqe0u6SUQGjp0qXu9iWXXHJeIDRwxhrrPXWVu63QUINVVGWmdmb/SsQBTSpZrwblk/bgAQAAAESRcn6tj2RB/wAcNmyYryJu0qRJbuqugjt/t956q2+SsSrN1IoaE62/qCpHVZgpLIzepqpWagVCCjBHjBgR8h+tao/Nli2bvfPOO25ysz9NC/YqKZ977jlXaRkO/wnSkaC2XwWKOtcdOnSw3377LUUcd7hUlXjNNde426o6DCR79uyuklRt7zt27HBt9qp8jYna28Wb3A0geelZv5wLCdOlPRf6KzzUYBUvRNR2Pa79AAAAAEQWQSJiHHjif9ujQE+hz9NPP+1uz58/31UcRg90VKnYvXt3t8Zfp06dXBgYfViIKs9Usdi+fXsXPKraLtA6SLfccou99dZbbo08hUwK0rTGof9wDg1PUburqtvUxjpx4kTXkh2IKib1Ohq+ouPUOo7Tpk07LxD11locOXKkvfjii1G2e4Ncdu3aZeHw9tu3b1/AQS/e6wV7TU001jl48skn7dixY+5z6vwqUIweFio8U4Wn1hGcPHlylO0HDx4M+7h1fr2fgd27dwfcxztuPR5oWIo+r//07WD0c6KQuFKlSjHup+9Ure6tW7e2lStXuopSTcMONIBFlYuqYJSsWbOGPAYASc/9P6VBeVvXq771alDOqhXNaSVyZ3HXun9ue3mWJgAAAACSgTRnYxqTiguS1xKr1l6tZehZtGiRqwZUIOOFQwqv1B6sdezUYhydQqrevXu7gEpBjSrEvPUNPQqiFOotXLjQDWvRaxYsWNAFR2o1vuqqq1xQqGqzUFRt+O2339qsWbNs2bJldvr0abemnqoP9boa3qFqSFWshUPHPWfOHBc2rVixwlVGqoJR6+rpePTHQ+dIw1rUTqy2bVVjar/169dHqZpU6/fnn39+3ns8+uij9vfff7tQy6PBHzpuBYNjxoxxk7DXrl3rq57Tmo76DJpkrAEx0SkMVLu3jl3t46q20/srGNV50HFXrlzZVXhWrVrVhaEKILXGpCr5ROe/fPnydvnll583zVjf2UMPPeTCQf+ATvtrAvZrr73m1m3U4BP/86BjVquxKkH1nU+YMME9ruBUj1WvXt0GDx4c43eiY1RIqNA5+s9odFu3bnUBsH4edJwKVBUie62T+jnT569bt667DkSVsGpxV8Vny5YtYzw2hG5TTa04L8FxboLj3ATHuQmM8xIc5yY4zk1wnJvAOC/BcW6C49yknnNDkJiKg0SFZ5qECyQFhaT6z004U0n1H9rff//dLr744rAC5vggSEzd/xNMKJyX4Dg3wXFuguPcBMZ5CY5zExznJjjOTWCcl+A4N8FxblLPuWHYSiqmaj4gqaiaM1z6D2ugSszEkFLWkAQAAAAAINJYIzEVCzWkArjQaRK4f2s2AAAAAAAIjorEVEjDKtRiqnXxtEah1jYcP358pA8LSDJay/K9995zw37Ucq01MRnGAgAAAABAzAgSUyENpgBSs1atWrkLAAAAAAAIH63NAAAAAAAAAEIiSAQAAAAAAAAQEkEiAAAAAAAAgJAIEgEAAAAAAACERJAIAAAAAAAAICSCRAAAAAAAAAAhESQCAAAAAAAACIkgEQAAAAAAAEBI6UPvAlxYnnvuOTtz5ozvfubMmd02pB6jRo2yn3/+2Y4cOWIHDhxw21566SWrW7dupA8NAAAAAIBkiyARqc6ECRPcdefOna1WrVqWO3fuSB8Skpi+e11OnjzpfgYOHjwY6UMCAAAAACDZI0hEqtWiRQsrUaKE7/7nn39ukydPtiVLltiJEyei7JstWzY7e/asC55UwViwYEGrVKmSXXvttda0aVPLkCFDyPdbt26dff311/b777+719HrqRru+PHjVqVKFRdoNWvWzHLlyhXleQsXLrTBgwfbli1bbN++fW5bpkyZrEyZMtaoUSPr0qXLee91zz332O7du23Dhg2WJk0aK1++vFWoUMGGDh163r6qzpw9e7bNnz/fFi9e7D57lixZ3HYdmz6vPutll11m1atXt+LFi7vnde/e3f766y9bs2aN77V0XnTR8ZYtW9bi6uOPP7ZPP/3Una9wKQz85JNPbObMmXbq1Cl3/PosOh59Tw0bNrSLLrooynP0vRUpUsQ9N5zvEAAAAACA1IwgEalW2rRRlwht1aqVuwwcONDGjx9vlStXtrFjx7pgzwuZFEwpnFPo9uGHH9p3331nb775pr3wwgtWo0aNgO+zd+9ee/75523p0qWuCu6hhx6yrFmz+h7funWrffnllzZs2DAXwD388MPWsWNHS5cunXu8Zs2aNmnSJBcM1q5d223r37+/3XrrrTEGcdKhQwcrVapU0NbtuXPnutdSkNa2bVt7/fXXrXDhwlH22bVrl82ZM8feeust69Wrl/vsefPm9YWSffr0sc8++8xy5sxpP/30k6VPH7//rCgAfO+999x51vGF0278v//9zx599FF3ftS2rOOTo0eP2ldffeW+03/++SfgefD/LgAASGqb9h6xNxdstCkrd9i+oyctd5YMdlPlgvZwrZJWIg//jwIAAMkLw1aAaLJnz+6uM2bMaPnz549SqaZtquxr166dq5Zr2bKlrV+/3h544AFXORioCvHmm292YeHEiRPttttuOy+4UkWcAkaFhTly5LARI0a4UExVdf7y5cvnu+0FZaHo+IO1bo8cOdIFjXpdVWIqwIweInqvoepNBYmq4Iz+3npcdOzxDRFFYaRCRHnnnXdC7q+gs1OnTla1alV75ZVXohyfKivvuusua926tZUuXTrg8xPimAEAiC11JgycscbKDpxpg2autSVbD9imfUfdte5rux7XfgAAAMkFQSIQRwoEBwwY4FqSjx07Zl27drWdO3f6Ht+zZ48L6g4dOmRDhgwJuRZjyZIlXQWkKhF//PFHF/TFl9qaA1HF5WuvvebafvU+hQoVCuvzFitWLOz3iCuFh2rZlnnz5tmqVati3F8t0Pv374+xQlPhsBcQh6pMBQAgKSgs7D11lZ0+cy4o1P9OC2bP6K5F2/W49gMAAEguKMUB4kEhlNplb7rpJtce/P7777u1A0UttqpEVNWit65gKFqLUGsuqtpx3Lhxdt9991mePHkS9Jg3btzoa0vWscamulGtz4npzz//tBUrVtisWbNs8+bNtnLlSnceVGkYzLJly9x1sKBQVPEJALjwjZy33kbPP1fVnpydPH3G1uw64rvfrV4Z616vrBXJmdm2HjhmQ2ets2Gz/naPKUwcv2iTZUiX1lUnHjt23G3PPGNugv8yLyF0ql3KutQJ3AUAAABSPoJEIJ40VERttcuXL7eff/7ZhXMapqJWZalfv36sXk/Bo4JEDTpRoBZTpV1cqIJPaz2qwrBx48ZhP0//WIkprEsIap9WG7XWW7z33nutd+/eNnXqVHdOA7Vde+3monOvoSoAkNztOHjcdh4+4f5fsW7/Sbct3fZDDH3yE9dzs2LbQVux/ZClJAoRhzSv6ruvMNG774WJ/qGjz4GoS6AkF/oOlm87mOTveyH/eSqQLaMVzJEp0ocBAIBDkAgkAE1FVpCogR6iqrrDhw+720WLFo3Va2k6skI7VR2EauuNCw0wkYoVK7o1BJMLTX/W+ogaYCPNmzd3LeEaVqPhKz169Aj4vBtuuMF++OEH++ijj9yalApHVdmpITPhVlsCQFIaNX+D9Z++OtrW/5bGgKWac6OCQlUiBqLtw2f/bSlticTRCza6S2RdWD8zfRpVsH6NK0b6MAAAcFgcDEgABQoUcNfegujbtm3zPZY5c+ZYvZbCPQ0uES+MTEjesQWr8IuUMWPGuAnNZcqUcfczZcpkd9xxh7s9YcIEt9ZkIM2aNbM777zT3dbAG03Ivvvuu93alZdffrmr8FRLttZRBAAguVWaqQIxEG3X4wAAAMkJQSKQQO00/hOMT58+7XvMfwBLuBSiSUKvjyhHjx6Ncp0caD3EKVOmWNu2baNsVyCoqcoKET/77LOgz+/fv7+bit2xY0e75ppr3OAatTwfOXLEVYq+8cYbLmxMjGAWAIC4Uou71kQMRNv1OAAAQHJCazOQAP799193fcUVV7hr/ynIaneuWbNm2K+lqka184pXnRdXCjSjTyVW9aSOd8uWLSGf365dO3csquY7c+aMuygs/fLLLxN0gXdNq1b4V6dOnSjbixQpYg0aNHCtyxpko+EzwdY8uvjii90l+mAZDWtRCLl+/Xr74osvzgsrASCpda5dylpVK+p+CbV6zbkW5wrlK1xQa7rFV1zPzSgNW4l4W2341MigwSr+ayR6tD2ltTVLp1olrXMEhq1cyH+eqEwFACQnBIlAPCn4W7x4sbvdpEkTd33JJZe4FmVV/c2bN89atWoV9utt2LDBTp065Srx6tWrF+Uxb+1E/4rHmBw7duy8dRAVdipIXLt2rW3atMlKlCgR9Pnjx49317/99pu1bt3a3f7kk08SNETctWuXG0yj8NVrUfanqkKFoWrJ1vqJsRk+o3BS1Yr6rP/73//cGooAEGkamqCLBl+d3nEu7KhSKLtveBQszuemSuEcbv+UNLXZG6gSbGqzlM+f9fypzZkzJcupzfoOqhY+t0RLUuLPEwAASYMgEYgnBYVqX65SpYpdf/31bpv+8tqoUSM3fXnmzJm2ffv2KFWKMdH+Xltv9NZmhYIK1g4cOBDWa+m48uXLF2Xb7bffbt9++637x4gGlPTq1Svk6/gfe0K3W6tisGDBgvb9999bunTpAu7z2GOPucffeeed84JEnQs9L1u2bEHfo1q1ai5IjH4uAAAXli51SrtLSjBwxhrrPfXcUDWFhhqsosoztTP7VyIOaFLJejUo7wvLli5d6vulJWEZAABIaqyRCMSD/kI/ePBgN1Dl5ZdfjlIZ0KVLF7fW4fHjx23QoEFhvZ7WAtSE4rJly7rwLLpixYq563CmOeu1tF/0dl8NIfHCuA8++MB+/fXXkK/l/7niOulZ7dFPPvlklG179uyxTz/91Dp06BA0RBQ9Ln/99ZfNmTMnymPvvvuuTZ06Ncb33rFjh/sMmugMAEBy0LN+ORcSpkt77v+xCg93HPovRNR2Pa79AAAAkguCRCCacNuGVRn46KOPuvbg0aNHW4UKFaI8XqpUKXvhhRdcgKVBIiNGjAgZSj7zzDOusk6Vd97kZn9e67TCt5jCRH2GAQMG2LXXXutCyehefPFF95j202dYsGBBjMemVmtP9IEl3qTqUBRYRl+vSJ8za9asdtttt8X43EsvvdSqV6/ubr/11lvnrYkU00AbfT8zZsywBx980CpVqhTWsQIAkNj09wNVGq7rVd96NShn1YrmtBK5s7hr3T+3vXyybF8GAACpF63NQICAMCYKrX788Uc3IEQtuZoWHCisk1tuucVy585tvXv3dmGj2pEeeeQR12rr/cNAA0xmzZrlAjKFkXo9hWuBPPTQQ7Z69WrX5nvXXXe517/qqquscOHClj17djcYZdmyZTZ58mQ3qOTVV18N+DoK9PR+am1WwNm+fXvX8qy1HBXaRf+8GlIi+izehGpPOG3WqsrUMfufJ63TqPdv2rRpWK1ZN998s1urcdGiRe58eetHqtLxww8/dBWN+gx58+aNcl5feuklFyKqQhQAgOSmRJ6sNqBJZXcBAABI7ggSAT8jR4504Zb8+eefduWVV7o1AXPlyuULGdXaq3WJ1K5co0aNkK+pwEvrHmpdQgVb3bt3d5WAmn6s6kO1PytYHDp0qK91ORgNYFE4OH/+fLf+ooI1va6Guuh1ihYt6oLAnj17Wt26dWN8LQ0wadOmjbVo0cIdn8JRVUR6n1GhoY5TlZEVK1Z01YMKLf2rCjWMZcKECb724TvuuMP3mJ6rKst9+/a5gSoKTvUZ5d5773UVlXovDVrRuVa7tdfC7G/69Oku9NT0a0/Xrl2tXLlyLuC855573DEqqFSrds6cOV2YqGCxcuXKNmbMGCtdOmWslwUAAAAAQHJGkIhUK1BLrqrWEqNyTRV3LVu2dJeEULt2bXdJCKpkVLWfLrGlEE+X2C72rmBQgaQuoVq2NLRGl2AUFuoCAAAAAAASF0EiUq1w10JEcHGdFhnThOWkFr1VGwAAAAAABMawFaRamzdvjvQhIMI02ZqfAwAAAAAAwkNFIlIdDSZRW3O/fv3cWnoabKK1/pB6vPHGGzZ16lS3tqTWiixUqJBbYxIAAAAAAARHkIhURwNPkLppWIsuAAAAAAAgfLQ2AwAAAAAAAAiJIBEAAAAAAABASASJAAAAAAAAAEIiSAQAAAAAAAAQEkEiAAAAAAAAgJAIEgEAAAAAAACERJAIAAAAAAAAIKT0oXcBkrfnnnvOzpw547ufOXNmtw0IZOfOndalSxff7dOnT1v58uVt3LhxkT40AAAAAACSNYJEpHgTJkxw1507d7ZatWpZ7ty5I31ISMYKFCjg+5kZO3asvfLKK1a0aNFIHxYAAAAAAMkeQSIuGC1atLASJUr47i9atMgGDRpkmzdvtn379rltmTJlsjJlylijRo18VWmeVatWWe/evW379u2uUk3SpUtn5cqVsxo1alj37t2ta9eu7rHVq1dHea72U1Xk2bNnfdv0XldddZULq7Zs2WKPPPKIrV271o4fP+4e79ixo3vNmAwfPty+//5727Bhg7tfqlQpd+xPPvmkPfDAA7Z06VLfZ9Mxvvvuu5YhQ4agrzdt2jR7/fXXbf369Xby5EkXqunzjR8/3t544w1buHChLVmyxHeM3mcTVe550qdP7z7fV199ZcWLF7fBgwfb3Llzbd26dXbq1Cm3T9q0ae2iiy6yHDly2J49e+zgwYNWpEgRq1mzprVv396Fd9p2//33u8d1jjz6nAULFrQPPvjAnnnmGZszZ47t2LHDPVa6dGn74osvLHv27EE/5x9//GF9+/a1jRs32tGjRy1Pnjzu/T7++GNXserRsUtM5wwAAAAAAJyT5qx/8gGkQBUrVnTXM2bMcMFVdLt377batWu72wq8brnllhhf78SJE66y8dChQy78U3gYXfPmzV2YqOshQ4a4bfqjdPjwYfc8hZfvvfee5c+f3wVaHgWJTZs29d0fMWKENWnSJORn7NSpk3v9N998M8r2I0eOuGPQ+8k999wT5f2CUXA4cuRIFxx6QaHnnXfesZdfftndXrZsmS9k03lR8KfQ79dff3WVfArs/L399tvufOg5CxYscCGiZ+XKldarVy93nS9fPlcV6H1f+mzNmjVz5+e6666zt956K8rrKsRs3bq1LV682N2//vrrbfTo0ZYmTZoYP+f06dPd96fPqTAxOgWUCmQV+Cq0RGj6OVCALZdccollzJgx0oeULHBeguPcBMe5CY5zExjnJTjOTXCcm+A4N4FxXoLj3ATHuUk954ZhK7jg5c2b13c7UJgUnf5Q58yZ091W4BWI1z6tyjyPQi1VyRUuXNiqV69u1157rQsS/an6z/+YFKwprAslV65cAVu2s2bN6irtvNdTxZ0uoejzKeSLHiL6f7bolXo6LzofWk/wjjvucNWM0XnHkSVLligholSuXNlGjRrlzpnCXQWW/ufO+24CfUc6TlUpeq//008/+QLcUJ8z2GtG//4AIDXbtPeIPTNlpV02dJaVevFHd6372g4AAAB4CBJxwQtVtZZQz4lOVZA33nhjwMf69evngjG13araUO3U8XHXXXdZ48aN3e0BAwa4NuPEpIDx2WefDXregrUKK/T0Kkj/+eefgM+NSb169axdu3butlrG1eIcH2q/BoDUTBXhA2essbIDZ9qgmWttydYDtmnfUXet+9qux2lgAQAAgPCvaCCRFCtWzMqWLRvwMVX9qQ1YFXZa++/hhx92bdHxCcTUanz55Ze7NQoff/xx1yacmBTqBRNTKHjs2DF37QWKsdWjRw9faKpA9pdffonT6wAAzIWFvaeustNnzgWF+s93wewZ3bVoux7XfgAAAAB9fUAiuPfee+3DDz+McR8NhtGah/fdd5+tWLHCDV7RuoWB2o3DoeEnWjdQ1YkazqJhLlqHMHp7dXxNnDjRBYUtW7aM9XO1LoQGsqhVu02bNvEKTRXAas1ErWP5ySefBA1tAeBCNnLeehs9/9xArtg6efqMrdn1X+tyt3plrHu9slYkZ2bbeuCYDZ21zobN+ts9pjBx/KJNliFd3H4H3al2KetSp3ScngsAAIDkgyARSGCquNO04HBUq1bNhg4d6sIwrfv30ksv2XPPPRfn99ZagKp0vPPOO90UZLVNa4iI/6Ti+FqzZo1VqFAh7EVltbaiWuLmzZvn2qELFSrkJkfrOq6ih6YPPfSQC03918MEgIS24+Bx23n4RIK93smTJ23d/pPudrrth+I0QX7FtoO2YvuheB+LQsQhzav67itM9O57YaJ/6BiX41y+7WCSnptgCmTLaAVzZEqw1wMAAEhNCBKRqgwcONDeeOONkPup2i0cmvqrwSOilmJNNVaApynD4WrYsKEbuqK1DVXFWLx4cd86gHHhX+m4ZMkSe+qpp+zVV1+N9XqA2t/7bAoCFZDqs6kFW+cxHJoOrXOk5yjkbN++vbVq1coNY4kv/9BU6y0qNH3//fddyAgAiWHU/A3Wf/rqRHr1nRYpamNWJWIg2j589t8W3yUSRy/Y6C7J4dz0aVTB+jWO2/IaAAAAqR1BIlIVBXaaphxK/fr1XWgWSosWLezJJ5+Msk0TiRUOxoZCP72fgreXX37ZLrrooli/RrBKx2nTptngwYOtZ8+esXoNVfepys/fmTNnzvu8MVF7tS6JxT80/eOPP3yhaUIMywGA1EIVeqpADETb9fiOQwlXiQkAAICUi2ErQALLly+fq5aL6xARL6z7888/43UcXqWjvPvuu67FOb5UpVi6dPJa48oLTXVsP/zwgwtiAQDhU7u21kQMRNsTsp0bAAAAKRsViUAimDlzZryHiGiSs4aIlCpVKs7H4V/pqPUXtS7hDTfcYPGhKsfkxr89XKFpkSJFrG3btpE+LAAXmM61S1mrakUT7PW0DuDqNedapSuUrxCndQBHadhKnFuGz1Hbsgar+K+R6NH2+LY1S6daJa1zLIatJMS5CUYVlgAAAIgbgkQgER04cMCtBxjuP4CiDxHp0KGDCxPjQ5WOW7duddV6mgw9btw4SwgapHL8+HHLkSOHJQf+oanWcFRlaIECBSJ9WAAuIBrQkZBDOvTf0dM7zv3/oUqh7G44VWxVKZzDPTe+U5u9gSrBpjZL+fxZ4zy1WcdZtXCOJD03AAAASHgEiUAievrpp90U5mLFioX9HLVFjxkzxg062bx5sz3wwAPu+bly5YrTMXiVjtu2bXPt0p07d7Zbb73V4uvnn3+21atXW9euXS250DqQOmc//vijux2foTUAkBJ0qVPaXeJq4Iw11nvqKndboaEGq6hiT+3M/pWIA5pUsl4NyifEIQMAACAFY41EIBEtW7YsTlVxJUuWtNdee81VMv71119xapWOXuk4cuRI1/KrydIJsV6iJkKrVTo50ZAVhaaVK1d2bXGa6gwACK5n/XIuJEyX9tyQKoWHGqzihYjarse1HwAAAECQiAvenj17fLd37doVcn8FUPv37/dNYA62TyiLFi1yrb/+7Vh79+511wrzQrn66qvthRdeCLnfvn37XAt1KAo033rrLcuePeYWuHA+m87P/Pnzzxsq432+Q4cOuba0cJ09e9Z9jpi+o3A/Z9asWd3nLFy4cNjvDwCplX4Bo0rDdb3qW68G5axa0ZxWIncWd63757aXd/sBAAAAtDbjgqUgb9CgQW7dPE/fvn1dNZ6Gc3Tp0iXK/qtWrbJnn33WtQArCJNRo0bZ9OnTrXr16q5F+ZtvvrF169bZypUr3eMfffSRff/995YtWzZLnz69ayM+deqU7dy50wWYF110kdvv8OHD1qZNG/v333/dfU1lVtVhnz597Morrwz6GVq0aOFeS1OJo9N6hxrKos+nY5o3b55bW1FrBQZTsWJFV5n40EMPnfeYPtOcOXPc5/FC1Ouuu84FjwpD06VL5yZK69zoPfWPSi+UVBXg3Llzbe3ate7+sWPHrH79+i7M03qF5csHbodToNq+fXv3Xt73pGPQ9OqCBQu670pTmFWRqTUjRY81atTIncNgVCk5duxYd869cBMAEFyJPFltQJPK7gIAAAAEQ5CIC4aq2vzVqFHDJk6cGPbzK1WqZF988UWM+zRv3txdP/7447E6NgWNkyZN8t1XIHf69GkXzoXSsWNHa9q06XnVkf7hoj67AsxwKkZq1qxps2fPtuXLl0fZrnZgXfR+sfXUU0+5S2xpUMvnn38ecliMLh59znAovFQAqUAZAAAAAADEH0EiLhgK5lIKVS7qEi4NW4lpYIsCxHAnQ4sGt9SuXdtSIlV+hkttzvXq1YtXGzcAAAAAADiHNRJxwdC0XiA21M4OAAAAAADCQ0UiUjytw6fW3n79+lnevHldFdr48eMjfVhIptQi3q5dOzcM5ujRo249Rf3cAAAAAACAmBEkIsWbNWtWpA8BKUi+fPnc0BwAAAAAABA7tDYDAAAAAAAACIkgEQAAAAAAAEBIBIkAAAAAAAAAQiJIBAAAAAAAABASQSIAAAAAAACAkAgSAQAAAAAAAIREkAgAAAAAAAAgJIJEAAAAAAAAACGlD70LkHo999xzdubMGd/9zJkzu21IuXbu3GldunTx3T59+rSVL1/exo0bF+lDAwAAAAAgWSNIBGIwYcIEd925c2erVauW5c6dO9KHhHgqUKCA73sdO3asvfLKK1a0aNFIHxYAAAAAAMkeQSIQhhYtWliJEiUCPrZw4UKbNGmS/fXXX5YpUyY7duyYpUmTxqpVq2YNGjSwOnXqWPr06e3FF1+0jz/+2IoXL27ZsmVz22Tt2rV2+PBhy5cvn1100UW+1z116pQtX77cPvroI/ceuixZssSOHz/u2yddunTuWlV1Hr2ujuOrr75y7xWKjunTTz+1r7/+OuS+3bt3d59zzZo1vm16Lx233nf79u128uRJK1KkiF177bX24IMPWt68eX376rN88MEHtn79el+Fp967cuXKQd/zxIkTdtddd9m///5re/fudc8pVaqU9enTx6688spYfw/+vPOTIUOGkJ8dAAAAAIDUjiARCEPatOcvJ6rw7plnnrF58+ZZ3759bfDgwb7HFGa9/PLL1rFjRxdw5cmTx7JmzWoPPfSQPfbYY1Fep02bNvbrr79akyZNzmubbtasmauC7Nq1q7u888477nVl2bJlvgBMYdvBgwdtz5497rVUZRdOiKi27ffee882bNhgc+fOtbp168a4/9ChQ911v3797JNPPrFy5crZ5MmTowRxc+bMcedDx/rTTz/ZF198YdmzZ3ePtW7d2l10rtRKrLBPbcbaxz9w9JcxY0YXEO7fv9+uuuoq99zGjRvH+Xvwp+8EAJAybNp7xN5csNGmrNxh+46etNxZMthNlQvaw7VKWok8/PccAAAgKTBsBYijV1991YVoo0aNskaNGkV5rGLFijZy5EgXAnrhlarhVEkXjAKzQG24qq7z+LdW+4d3eq4qGrXW3x133OGeFw4FfQoRRcFfuLzQT5WV0av5rrnmGhs2bJi7rcrDiRMnnvd8BZAK8VRduGXLFnvkkUdcGBqTXLlyuevo7eWx/R78Ra9QBAAkP2fPnrWBM9ZY2YEzbdDMtbZk6wHbtO+ou9Z9bdfj2g8AAACJiyARiIMjR464Ft2yZcvapZdeGnCfLFmyRAm9rr/+erv66quDvqbXpuyvQ4cOAQOwmCjYe/bZZ8PaV+GhF76pom/VqlVhPU8twzG57LLLLEeOHO62F1RGp8+lCkdVe/72229xGmITl+8hVKUpACB5eWXWeus9dZWdPnMuKNT/ggpmz+iuRdv1uEJFAAAAJC7KcYA4UKWdQiyvZTeYH374wXf7kksuifX7hGo1DqZevXoh9/nzzz9txYoVNmvWLNu8ebOtXLnStRurLTq+1G7srduo6sNgGjZsaL169bIBAwa4qkJVbHbq1ClRvwcAQNyNnLfeRs8P/AuihKTqwmPHjtupM2ftn0P/rQPcrV4Z616vrBXJmdm2HjhmQ2ets2Gz/naPKUwcv2iTZUiXtL8k6lS7lHWpUzpJ3xMAACBSCBKBOPDakLUGn4aAJJepv2ojVrVgy5YtQ+771ltvuSEyOXPmtHvvvdd69+5tU6dOdQNVChcuHK/j+PHHH13AV6xYMbvlllti3Pe+++5z7c3jx493bcoaatO0adMU/T0ASF12HDxuOw/HvDxDIBpOtW7/SXc73fZDKWLw04ptB23F9kMReW+FiEOaV/XdV5jo3ffCxDW7jkTknCzfdjBJ3is5/cwUyJbRCub4b/kVAACQOhAkAnGgKrvSpUu7ijiFcQruqlev7rZpgnGg9Q6TgqYpV6hQIaz9tD7id9995+43b97chgwZ4qYia/hKjx494lxBMm3aNBdK6jhee+21kNWCovfbunWrqxzs2bOnCzL9JzKntO8BQOoyav4G6z99dTxfZWcCHc2FSW3MqkQMRNuHz/7bIrVE4ugFG90l6UX2Z6ZPowrWr3HFiB4DAABIegSJQByo6k/r+2kK886dO91ag96wEj1WpEgR18p85513Wp06dRL8/bW2n4aq/Nf+dcxV9R0+fNgGDhwY8vljxoxxbdNlypRx9zXQRa+nKsUJEya4ScrhBIDyzz//uPc8evSoC/Q0WXnQoEFWv379sNcg1H5qqd6xY4ctXrzYvf9nn31mJUuWTNbfAwAg6arfVIEYiLbr8R2HYl8VCgAAgNhh0gAQR1WrVrXvv//ere936623usBK04wV7KnNVtV17du3d8FcQtP76HV1+fzzz+2bb75xA0vCaQnWeohTpkyxtm3bRtl+9913uynGhw4dciFeuIoXL+7akefPn++qCmvUqOHWPoztIBOFmaNHj3brJKoysmPHju46OX8PAICkodZxrYkYiLbHpbUcAAAAsUdFIhAPqtq7/fbb3cWj6kANMFGVnoK1YcOG2W233RZwKnNCUnCnlt5Qxo4d6yr9olfoqXqvQYMGLnh7//333dqF4a69pNZkBZTTp0+3559/3rUcX3755bH+DJrk/Pbbb7sKQk177ty5s1s7USFjSvkeAKQ+nWuXslbVisZpvbvVa861RFcoXyFFrJE4SsNWItDGq7ZlDVbxXyPRo+2RamuWTrVKWuckGraSnH5mVAUKAABSH4JEIIFlzpzZGjdu7IaYtGvXzlXV7dmzxwoUKJDo7/3II4/E+PiuXbts0qRJVqhQIRfWRacBKQokt23b5tZPVIVfONRGrNZkVTVq+rOOQ4Nf9D6xperGN9980wWZv//+uz355JNuCEtsKxwj+T0ASF00cCIuQydOnDhhp3ecC4KqFMqeItZ1rVI4hzvWSExt9gaqBJvaLOXzZ03yqc06J1UL50iS90qJPzMAAODCQpAIxJHCtpimG1966aXuWu3CuXLlSsIjO/cPjePHj1uOHFH/YTNu3DgrWLCgawUOVpn32GOPuce11mC4QaJkyZLFRo4c6QaeaL1ChYkffvhhnP6RU61aNbf2oV5Dw1tU5ahLSvseAOBC06VOaXdJiv+PLV261N2eujub9Zm2xt1WaKjBKqqGUzuzfyXigCaVrFeD8ol+bAAAAKkZayQCcXDmzBm76667YtxHYZqoXTipKwZ+/vlnN33Zn6rxPv30U+vQoUOM7b16XP766y+bM2dOrN63WLFiNnjwYFeh+Oeff1rfvn3j+AnMrbPoTY/WcasqMaV9DwCA+HuqXmkXEqZLm8bdV3iowSpeiKjterxn/XKRPVAAAIBUgCARiOMaRfv373cVE8Gooi9fvnzWs2dPS2pLliw5r61Yx5M1a1a3TmBMVMFXvXp1d1tTnGOrXr169uCDD7rbaqPWGodxpZZkbzr1qFGjUtz3AACIP/1ySpWG63rVt14Nylm1ojmtRO4s7lr3z20v7/YDAABA4iJIBOJg3759bj1BTQNetGiRW8vJs2PHDuvXr5/98ccfrrW3aNHgC/DreaoUlN27d4d8XwVnoShY0wRlDS7xaHrxRx99ZNdff31YVXk333yzu9Zn08CS6LxpyjoPqgqM7vHHH7errrrK3VaFYvTX0PMOHz4c8LnRqaqxbt26ifo9AACSvxJ5stqAJpVtcbd6tuHZhu5a97UdAAAASYM1EoE40Fp7AwYMsJkzZ1rv3r3deoRap08hnR5r1KiR2x5smuLBgwdd+KXwcMuWLb7qvWXLllmFChXc+oD+NMBEbcZau1D0vOuuu85NK9Z7qlVZodyhQ4fc66kqQ4/Jvffea6tWrXKBm95DLcda+9BrYfanqcuqQvznn39827p27eqmMH/xxRf29NNP2+rVq23NmnNrVW3cuNG1IKviT5V/3pqMOp4RI0a4KcoKMfUaTZo0cdWKGqSyfv16F4rqftmyZWOsWtTahmprVnWit15WQn0PAAAAAAAgfASJQBj8K928icAKyXSJCwVun3/+edj7V65c2V06duwY6/dSMKggTZdQbV8K3nQJJnrAGROFiz/99JPv/qlTp1wo2LRpU9+206dPh1WVqFBU6yQqaNU6jAn1PQAAAAAAgPARJAJhUOCVUmXLls2SA4WI0alyMabBL9Gff9lllyXoMYXTKg4AAAAAAM5hjUQgDJs3b470ISARqOUbAAAAAACEh4pEIAZab09tzRrakTdvXjf1OD5TiBF5Wl9S6y1q0vPRo0fddGt9twAAAAAAIGYEiUAMAk0sRsqmtRu/+eabSB8GAAAAAAApDq3NAAAAAAAAAEIiSAQAAAAAAAAQEkEiAAAAAAAAgJAIEgEAAAAAAACERJAIAAAAAAAAICSCRAAAAAAAAAAhESQCAAAAAAAACCl96F0AxOS5556zM2fO+O5nzpzZbUPy9fzzz9vy5cvtwIEDduTIEbftnXfesXLlykX60AAAAAAASLYIEoF4mjBhgrvu3Lmz1apVy3Lnzh3pQ0oVtm3bZmnSpLFChQrF+rl9+/Z113v27HHfmaRLly7BjxEAAAAAgAsJQSKQQFq0aGElSpTw3V+4cKENHjzYtmzZYvv27fNtV/CVL18+279/vwuysmfPbhUrVrTWrVtb/fr1Y/Wejz/+uOXPn9+effbZGPfbsGGDde/e3bZv3247d+5022677TZ76aWXwn4vPVfHd+rUKXdf1XslS5a0UaNGRXmfTz/91H799VfLkCGDHTt2zFVrli5d2j1Xl5w5c9rixYvttddes/Xr19vWrVt9z0+bNq0LB0+fPu3bpvuq8nzooYesU6dO9vLLL9ukSZPcOc2UKZOrJKxevXqMxz5lyhR74okn3Dnu06ePb3vevHkta9asripRxwsAAAAAAIJjjUQggSgE81ezZk0XeCnE8gwZMsRmz55tX375pc2cOdN++ukna9y4sc2dO9eFZJ999lnY76eActq0aTZx4kQXSsakVKlSbj+9n4JL+frrr11VX7jGjx/vCxFbtmxp3333XZQQ8aOPPnJhqsI5hYn6LF999ZWNGzfOChcubD169LAFCxa4fS+//HJ799133XnwvPjii7Zy5UpbsWKFuyiMnDFjhr311ltWrFgx9xlErzNmzBgrWrSoHT9+3AWEe/fuDXrcBw8edIHplVdeab169Trv8WzZsoV9DgCkPpv2HrFnpqy0y4bOslIv/uiudV/bAQAAgNSGIBFIZArWPLly5YryWJ48edx6inXr1nX3hw0bFmW9xZi8//77rnJP1XSffPJJWM9R1Z0qAtOnT28nT550gVw4FNQpGCxQoIC7r4pKf3PmzLEXXnjBtXd37NjRMmbM6HusYMGC9swzz9gll1ziKhOjf36Pf2uxbutcXXTRRVavXj2rXLmy772lWrVqNnbsWFdNuGPHDrfmYTA6p/Lqq68GrDrUuQCA6M6ePWsDZ6yxsgNn2qCZa23J1gO2ad9Rd6372q7HtR8AAACQWhAkAolMrbmhXHPNNe5a7br+bdAxVdl9/vnn1qhRI3f/ww8/tBMnToR9PLfccou7rddQy3I41YgKO6MHgR4FkvrHtCoVg1GAqeAvLtq0aWPly5ePsq1s2bLWv39/d3vq1Kmu0jO6JUuWuDUsVQnqH0TGVEkKAKKwsPfUVXb6zLmgUP8pL5g9o7sWbdfj2g8AAABILSjFAZIBVQd6lX7+VXrBqAJR6yqqEu/nn3926x6qjbhVq1ZhvV/z5s3tjz/+sHXr1tno0aOtX79+MVYjqm1Z7+kFd9FpArJ4bdOBaC3DuFIFYrDPMX36dPvhhx9swIABrp3cq4ZUG7aqPe+//363HUDKN3Leehs9f0Oiv8/J02dsza7/Wpe71Stj3euVtSI5M9vWA8ds6Kx1NmzW3+4xhYnjF22yDOli/0sJ/QLm2LHj7nbmGXNj/MVTp9qlrEudwL/MAQAAAJIKQSIQYWpP1nqDotbgUBWMWhfwvffecyGZgsebbrrJhYhac/D2228PqwJSVXiPPvqoPfbYY/bFF1/Ygw8+6NYhDERrHF577bXnVQT6U3h3+PBht+5jkyZNLCHPjYJAtXEHo+Epv/zyixv0ov0eeOABX3Cp5+tzAheyHQeP287D4VUkR+oXJev2n/tlSbrth+I12GjFtoO2YvshS0oKEYc0r+q7rzDRu++Fif6hY5wdOLcGbUyfffm2g3ahK5AtoxXMkSnShwEAAIAgCBKBCFIloar81qxZ4wKve++9N+RzFPwpuPPamtX2qyBR1YWqTrz++uvDem8NealSpYobbDJy5MiAE5w1VVqViGqBjskNN9zg1lB86qmn3EAXtUEreCxevHiMVYqh6LyEWn9MU6uffvpptw7jm2++6aZRa/iMKi0VuPqv1whciEbN32D9p6+2lOHc1PiUQr+XUSViINo+fPbfllRLJI5esNFdLnR9GlWwfo0rRvowAAAAEARBIpDEFHYtXrzYhYiqmFMIpxCsSJEiIZ+rdl1VCN5zzz2+4SQaYqIpyHpNVeGFGySqclHh5cMPP+yCSA1J8SYje95++21r2LChlSlTJsbX6t69u61atcr+/PNP91q6eNSqrTbspk2bupDPf6iKP01n1rRnr4Jp9+7dbv3Gq666KuRn0etOnjzZTXpWKKpW6zvuuMMuvfTSsM4FAASrjlMFYiDarsd3HEq+1aAAAABAQmPKAJDE1Iq8dOlSF3YpMGvbtm1YIaKoBVpVggrJ/HmVjArS9NrhUuh42WWXuYDy9ddfj/LYrl273KCSLl26hHwdTVhWCKgwUCFnjRo1rHDhwi401BqLCxcudK3YqhwMpnfv3u79dPnyyy/d8JRwp0p7Lc56vw8++MA2b97s2rYBID7UMq41EQPR9uTcUg4AAAAkBioSgSRWokQJGzVqlN13332uHVntwLofam1DtfiqQlATlxXcRW9Tfvnll23Hjh2uYnHEiBFhH8/jjz9u7dq1sylTprjqRG8tRIWCWu9QxxsOrbt43XXXuYtHFZeanDx06FBbtGiRffvtt249xkqVKoX1mprMHC4dt4ay/P77767tO64TooGUpnPtUtaqWlFLrvQLk9VrzrVeVyhfIV5rJI7SsJUkbO9V27IGq/ivkejR9qRqa5ZOtUpa51QwbEVVngAAAEi+CBKBCFAVoKYMP/nkk25AiaoBQw0FmTFjhgseFdhFr0gU7x/n06ZNs3/++cetTxiOWrVq2dVXX+0Glrz22mvuWNRSPGnSJPv6668tPlQhqLZrrVeogS1HjhxxnyHcIPGiiy5yFYbhSp/+3H/SCBGRmmgwRXIeTnHixAk7vePcf5+qFMoer3VLqxTO4V4jKac2ewNVgk1tlvL5s8Z/anPmTDH+QkmfvWrhHHH4NAAAAEDCIUgEIqR58+auvVnTllWRWLlyZd8AlWBrK958882u8jBY1U+DBg1cCKghI88++2ysqhLvvvtumz59ujumiRMnuuMLNsk5Oq33qLUQvSAvuhw5cljp0qXda+fNm9fiQi3dcX0ugAtDlzql3SUpDJyxxnpPXeVuKzTUYBVVy6md2b8ScUCTStarQfCp9qFCVm85Cq13y3AoAAAAJHeskQhEkCoSr7jiCleVovUDNaU4kFmzZtmyZcvcQJRgVJGoCc6iIHDfvn1hH4eOQRWDOo4XXnjBDS5Rm3O4FERu3bo16ON6Xa25WKBAAatevXrYr+v//Pvvvz/WzwOAuOpZv5wLCdOlPVclqPBQg1W8EFHb9bj2AwAAAFILgkQgglTBN3z4cFfNp7bfzp07BwwAVbGoasNQawbeddddli1bNvda77//fqyORWGgaPpzs2bN3LCUcKkaUuszxjQkRkHiiy++GKf10f7++2/alQEkKbUZq9JwXa/61qtBOatWNKeVyJ3FXev+ue3lQ65vCwAAAFxICBKBRKaW3EC3PQrshgwZ4tY+3LRpk3Xt2tW1u3l++OEH++OPP9xAlVDUQqxJzKIgUS3H/jSd+eDBgy7Ui65q1aqutVr/KA5U/aeqQO/4d+/eHeWx/fv3u6ExGqZy/Pi59b7ECzT1+bT2ov8gFvH/nMFoYItarkO1Ncd0fAAQVyXyZLUBTSrb4m71bMOzDd217ms7AAAAkNqwRiKQSBYuXGivvPKKbdmyxbetT58+bv1CrT/Yvn173/a6devaE0884ZtufOONN7rnqoJv9epz00779u3r1lPs0aOH1axZ87z306Tm2bNn+9qjFRg2bdrUbrjhBnvggQdcG7XWTzxw4ID16tXLxo4dazfddJObouxRRaTCRq1n6FGFpPZROOd9Fg1i0bpeJUuWtJEjR7rX05AXreOoNRzz589v2bNnt8yZM7tBLmqVzp07d5QA8eOPP/Z9NlEQqX2yZMniKjUVaCqUVMu0Aslbb7014Hk+fPiwmzod6PjKlCnjBsgAAAAAAID4I0gEEogq4vwp7NNaheHS+ofeGohnzpxxr/fhhx+6xffDaQdWa7LXnhzIF198EfI1qlSp4sJAfwr3Pv/88xifp0rD6NWGMdFnUvgnL730ksWHWrlDHR8AAAAAAIg/gkQggagFN6GozdkLyZC4wmmvBgAAAAAArJEIJJjNmzdH+hAQS2r13rt3b6QPAwAAAACAFIGKRCCeNCxFbcj9+vVzA0E0XXj8+PGRPizEQGtV/vbbb27txXz58rltcZkmDQAAAABAakKQCMTTrFmzIn0IiKX+/ftH+hAAAAAAAEhxaG0GAAAAAAAAEBJBIgAAAAAAAICQCBIBAAAAAAAAhESQCAAAAAAAACAkgkQAAAAAAAAAIREkAgAAAAAAAAiJIBEAAAAAAABASOlD7wJAnnvuOTtz5ozvfubMmd02pDx33nmnnT171nbt2mWnTp2yHDly2HfffRfpwwIAAAAAIFkjSATCNGHCBHfduXNnq1WrluXOnTvSh4Q4+uyzz9z11KlT7fHHH48SEAMAAAAAgMAIEoFYatGihZUoUSLgYwsXLrRJkybZX3/9ZZkyZbJjx45ZmjRprFq1atagQQOrU6eOpU+f3l588UX7+OOPrXjx4pYtWza3TdauXWuHDx+2fPny2UUXXeR7XVXNLV++3D766COrXr16wPceNGiQzZ8/39atW+f2lzx58ljRokXt6aeftpo1a8bqc+q1pk2bZqtWrXKf4fjx43bw4EHLkCGDXXrppVavXj1r2LChu+9v3Lhx9u2337rj0HNEoWuxYsXs0Ucfteuuuy7K/mvWrLGePXva9u3bbefOna7Ss0yZMtasWTMrUKCAjR8/3jZs2ODOi/g/3qFDB9/rLFu2zIW9S5YssYwZM7r3VkBYuXJld+51vHquP51/0f4AAAAAACBmBIlALKVNe/7SoqdPn7ZnnnnG5s2bZ3379rXBgwf7HlOo+PLLL1vHjh1d0KhwL2vWrPbQQw/ZY489FuV12rRpY7/++qs1adLkvLZpBWcxVUEqjJMxY8bY0KFD3e0pU6ZY3rx5Y/X5FBzqs2TJksUdc58+fXyfWe3AK1eudCFot27dXNCn42zUqJHv+Qr3dNF7P/HEE27bl19+6QLNQMqXL28TJ060EydOuErPl156yRo3bux7/Oabb3bhYKtWrdx9ncsbb7wxymsMGzbMPvnkE3vqqaesX79+vuP9559/bMSIES7A1HtcfPHFUZ6n7wEAAAAAAISHYStAAnj11Vdt8uTJNmrUqCihmlSsWNFGjhzpQkCFiKIKxFKlSgV9vUAVcgrtVOUYiqoZPbENEdXqq8BOFYfvv/++q+LzD05VmVilShVXUfn666/b7t27rWvXrq4KMabj8L8d02fOmTNnwLDUO2/Rb3ttym+99ZY9//zzdscdd0Q5XlUcKlTV+5cuXfq8141eTQkktk17j9gzU1baZUNnWakXf3TXuq/tAAAAAJDcESQC8XTkyBH74IMPrGzZsi6AC0TVff4B2fXXX29XX3110NdMly7dedtU5Rc9RAtEYV9c/Pbbb66iT5+jd+/eAY/Bn9qae/Xq5W6/8sorrhU6voIde0yfSSGiKgv9qxij0zCVQNWHcT1XQGypmnfgjDVWduBMGzRzrS3ZesA27TvqrnVf2/W49gMAAACA5IogEYin9evXuzAxe/bsMe73ww8/+G5fcsklVrhw4Vi9T926dUO+R1wpvFCV4cmTJ61du3ZhV+pp+rHWctTz1V6c1Pbt22dbtmxxIWFMwafOPaEhIklhYe+pq+z0mXNBoX4cC2bP6K5F2/W49gMAAACA5Io1EoF48tqQtRbiv//+G3QtwORMaxBq7UOvWjJcChy1hqFaupcuXZrkn18t4goId+3a5T5DsIpQIL5Gzltvo+dvCPq4wvRjx84NF8o8Y26U4Prk6TO2Ztd/rcvd6pWx7vXKWpGcmW3rgWM2dNY6Gzbrb/eYwsTxizZZhnSx/z1fp9qlrEud81v4AQAAACChECQC8VSuXDm3/p4qEzXRuWXLlm6ysrapWi8lTAT+5Zdf3LUmSOfKlStWz7388sujDGpJyiBRFZqahD137lxr27atCzU1sEVTnbU+olrKkfLsOHjcdh4+YcnJim0HbcX2Q+HtfODc1PRAFCIOaV7Vd19honffCxP9Q8fYHuPybQct0gpky2gFc4RezxUAAABAykOQCMSTKo800ENTmHfu3GnvvPOOu3iPFSlSxLUyqw1YoVdytH37dncdl+CtYMGCvtuHDx+2pNa/f39r3769bdiwwT799FN38R9Qo+Ewt956q910001JfmyIm1HzN1j/6avtQqMiRVUiBqLtw2f/bfFZInH0go3uEml9GlWwfo0rRvowAAAAACQCgkQgAVStWtW+//57d1m0aJGtW7fOrd23Z88e1+6ri9bpe+GFF9xk4eTm9OnT7lrHq9uhBq3486+4DGcYTEIrVqyYffPNN/bjjz/avHnz3LnfvHmza3dWsDtr1ix3Uev5E088keTHB/hX6qkCMRBt1+M7DiWvSkwAAAAA8EeQCCRgm+3tt9/uLp5jx465EGvgwIG2detWN5Dktttui1VQlxQKFSrkrs+cOeMC0BIlSoT9XIWPHrVzx0dsQ0z/MFMVh/5VhydOnHCh7qBBg2z16tU2duxYN0gmEmEnIGrX1pqIgcJEbU9u7dwAAAAAEB1BIpCIMmfObI0bN7acOXO6EGvv3r0ueFPLbXJSo0YN322tN3jPPfeE/VxVAIpaiFUd6Emb9r9hEadOnbJMmUKvmabgVecsIShcVCv5yJEjrVGjRu4YNm7cSJCYAnSuXcpaVUteQ4tGadhKPNuG1baswSr+ayR6tD0+bc3SqVZJ65wMhq2oshIAAADAhYkgEUgA27Zts8KFCwd93JsmrCnDsR1mkhQuu+wyNxhGLcGfffaZ3X333VGmzsZk5syZ7rpz585Rtvuvt3jw4EE3yCUmx48ftwMHDlj+/PkT9NyrujJ37ty2b98+y5cvX6xeG5GhQR3JbVhHlcI5rEqh7OFNbc6cKejUZm+gSrCpzVI+f9Y4TW3WMVYtnCPWzwMAAACAcBEkAvGkduC77rrLfv7556D7aK0+adCgQbKZ4qzgo1OnTvbmm2+6gPORRx6xHj16uMnLGliiMDGUJUuW2OzZs61Zs2au6s+f//RmvWZMYZ/8/vvvLujz2qzDpXOvMNO/AtLfkSNH3BAYrWOpSc5AXHSpU9pdglEr/dKlS91tDVeK/ud84Iw11nvqKndboaEGq6hyT+3M/pWIA5pUsl4NyifWxwAAAACAeIl9yQOAKE6ePGn79+93QUIwmuKskKxnz55JEhCGY+XKlVGmLGuysTcIZsCAATEGo14loD6PwlGtQxhd3rx5rWbNmu728OHDXbVhMKoW1Gso2Ay3EtL//HtBbSAffPCBZciQwU13BiKlZ/1yLiRMl/bcz7f+mGqwivfHVdv1uPYDAAAAgOSKikQgnhSCqeqtffv29thjj1n16tV9YdiOHTts1KhR9scff9iHH34YpUovUADoDS7ZvXt3nI8npsDOP3z77rvvzhuqorBNVXtaV1ChntZK1NqO/pV8+qxff/21vf/++y54bNu2bdDwb/DgwfbAAw+4isSmTZtaq1atrHLlyq46UeHe9u3b3UCUyZMnuyE0rVu3Dvg6WlvS439udM4U4j744IPWrVs3q1u3rquuFG1/77333ERnBbkXX3xxGGcPSBz6M6JKw9ZXFLO3Fm60KSt32N4jJy1P1gx2U+WC9lDNklYiT9ZIHyYAAAAAxIggEYgnrXmoCj611/bu3dut9aegTK2Nekwtv9qu4CwQrR+oEFIBmSYmy6RJk2zZsmVWoUIFGzp0aNjHonBw9OjRvvtehaE3EVkBooJGVfDp/tNPP31e2NGxY0c3eXrixIm2cOFCFyZqUErBggXdRGVVGl511VU2YcIEN6k6JmpT1utMmTLFpk+f7gLDd99911Vvas1EBZka9KJt5cuf386pEFCPaUiKp1evXvb2229b8+bNXcj58ssv24wZM1xFo0JOHafWZ9Tr16tXz7766ivLmpWABsmDwsIBTSq7CwAAAACkNASJQDxbhzVlWMGbLnGRI0cO+/zzzxPk2BRa6qLQMrYtwv4UFqrKT5f4UqCqtmldYkthoS4xUaWjLgAAAAAAIHERJAKxpEq+5Cq5DHJJKVShCQAAAAAAwsOwFSCWNm/eHOlDQALRwBkAAAAAABAeKhKBMGndQ7U19+vXz7X+at298ePHR/qwEActWrSwo0ePuovWcdS6igAAAAAAIGYEiUCYZs2aFelDQAL58ssvI30IAAAAAACkOLQ2AwAAAAAAAAiJIBEAAAAAAABASASJAAAAAAAAAEIiSAQAAAAAAAAQEkEiAAAAAAAAgJAIEgEAAAAAAACERJAIAAAAAAAAICSCRAAAAAAAAAAhESQCAAAAAAAACIkgEQAAAAAAAEBIBIkAAAAAAAAAQiJIBAAAAAAAABASQSIAAAAAAACAkAgSAQAAAAAAAIREkAgAAAAAAAAgJIJEAAAAAAAAACERJAIAAAAAAAAIiSARAAAAAAAAQEgEiQAAAAAAAABCIkgEAAAAAAAAEBJBIgAAAAAAAICQCBIBAAAAAAAAhESQCAAAAAAAACAkgkQAAAAAAAAAIREkAgAAAAAAAAiJIBEAAAAAAABASASJAAAAAAAAAEIiSASQ5M6ePRvpQwAAAAAAALGUPrZPAIBwLVq0yPr06WPbtm2zDh06WObMmW3x4sV2/PhxGzt2bKQPDwAAAAAAxAJBIoBEMXfuXHvssccsZ86cduzYMZs/f76dOXPGBYlXXXVVpA8PAAAAAADEEkEigAS3YsUK6927t02ePNmKFy9uGzZssBw5ctjMmTNdkAgAAAAAAFIegkQACb7+YY8ePaxq1aouRJRSpUq563Tp0kX46HAh2rT3iL25YKNNWbnD9h09abmzZLCbKhe0h2uVtBJ5skb68AAAAADggkGQCCBB/fHHH7Z69WqrXLlypA8FqSC0HjRzrfX54S87fea/AT6b9h21JVsP2Cs/r7P+jStaz/rlLE2aNBE9VgAAAAC4EDC1GUCC+vfff9112rT85wWJSyFi76mrfCGissKC2TO6a9F2Pa79AAAAAADxR0UigAR14sSJOD93x44d9t5779kvv/zi2qA17VlrK9avX9/uv/9+y5Mnz3nP0QTozz77zKZNm+bee+/evXb69Gm7+uqr3aTocuXK+fY9dOiQrV271pYtW2azZ8+266+/3po3b24vvvii/fjjj3bxxRfb+PHj43z8iJ+R89bb6Pkbwtr35OkztmbXEd/9bvXKWPd6Za1Izsy29cAxGzprnQ2b9bd7TGHi+EWbLEO68MLtTrVLWZc6peP4KQAAAADgwkWQCCBBnDp1ygV6EyZMcPd//vlnu+OOO9z2KlWquLAuJgsWLLDHH3/cWrZsaR9//LFlzJjRPfeTTz6xgQMH2pdffmljx461ihUr+p6zdetW69ixoxUoUMBGjBhh+fPn973WE088Yd9++617brNmzXzb58yZY9OnT7c9e/a4gFKTpbVd4ePJkycT9RylJDsOHredh88PhXWO1u0/d57SbT9kGTJkSLD3XLHtoK3YfijWz1OIOKR5Vd99hYnefS9M9A8dwzmO5dsOxuoYEuq8FMiW0QrmyBSn5wIAAABAYiNIBJAg0qdPb61bt3YB3apVq+y6666zQYMGhfXcf/75x7p06WKXX365G9Ti/5pt2rSxnTt32ltvvWWdOnWyqVOnWqZMmVzI+PDDD7sqxk8//dSyZcvme16tWrVsyJAhriKxZ8+eVqFCBXdp1KiRuxw7dsy++uorGzdunD366KPutRcuXOgCTJwzav4G6z99dYi9dlqkqY1ZlYiBaPvw2X/b2f+WTwzL6AUb3SXu4n5e+jSqYP0a/xeWAwAAAEBywiJmACJOQd7hw4etYcOGAR9v3769a3XesmWLCxJlypQpLrCsXbt2lBDRU7duXTfwRZVi7777bpTHvPUbL730UtfarMBS+48aNSpRPh8Sjyr4VIEYiLbrcQAAAABAwiBIBBBxc+fOddeFCxcO+Hju3LmtatWqvqnQ4TxH6tSp467//PPPgI97r4mUS+3XWhMxEG0P1J4NAAAAAIgbWpsBRNyuXbvc9dGjR4PuU6hQIXftrWO4e/dud33kyJGwnxNdlixZ4nHUF7bOtUtZq2pFz9uuc7l6zbmW5wrlKyToGomjNGwlli3FalvWYBX/NRI92h7btmbpVKukdY7lsJWEOi9UUAIAAABIzggSAUScKg61DuLff58bjBGI1kWUEiVKuOtcuXK56/Xr14f9HIRPAz8CDf3QZOzTO86FZFUKZXdDcRJKlcI53GvGdmqzN1Al2NRmKZ8/a9hTm3UcVQvniNWxJ+Z5AQAAAIDkgiARQMRdccUV9sMPP7h25a5duwbc599//3VrG950002+53z33Xeu1fnQoUOWPXv2gM+Rpk2bJvInQELoUqe0u4Rr4Iw11nvqKndboaEGq6iiT+3M/pWIA5pUsl4NyifGIQMAAABAqsIaiQAiTtOeZfHixbZo0aLzHteQlSVLlth9991nxYsXd9uaNWvmKhmPHz9u48ePP+85p0+ftunTp1u1atXcvrjw9KxfzoWE6dKmcfcVHu449F+IqO16XPsBAAAAAOKPIBFAgvLWLAy0dqG3BqImNPu7+uqr7dFHH3W3db1w4ULfYxs3bnRVirfddps9/fTTvu0KEYcOHWqZM2d205Y/+ugjO3XqlHtMFYraV/vosehtpnrc/3iQMqVJk8ZVGq7rVd96NShn1YrmtBK5s7hr3T+3vbzbDwAAAAAQf7Q2A0gQqv4bM2aMrV59buDEtGnTXEtx1qxZrWfPnvbyyy/71jNcvny5tWzZ0urWrWvdunVz27p06WIXX3yxvffeey5MVAio6sP8+fPbM888YzVq1DjvPfX8iRMn2ujRo32XChUquLURmzRp4ioR1Q7t0TH88ssvtmrVuXbYESNG2FdffWUVK1a0wYMHJ9GZQkIrkSerDWhS2V0AAAAAAImHIBFAgmjUqJG7BDNhwoSQr1GvXj13iY1y5cq5ysRw9OjRI1avDQAAAAAA/kNrMwAAAAAAAICQCBIBAAAAAAAAhESQCAAAAAAAACAkgkQAAAAAAAAAIREkAgAAAAAAAAiJIBEAAAAAAABASASJAAAAAAAAAEIiSAQAAAAAAAAQEkEiAAAAAAAAgJAIEgEAAAAAAACERJAIAAAAAAAAICSCRAAAAAAAAAAhESQCAAAAAAAACIkgEQAAAAAAAEBIBIkAAAAAAAAAQiJIBAAAAAAAABASQSIAAAAAAACAkAgSAQAAAAAAAIREkAgAAAAAAAAgJIJEAAAAAAAAACERJAIAAAAAAAAIiSARAAAAAAAAQEgEiQAAAAAAAABCIkgEAAAAAAAAEBJBIgAAAAAAAICQCBIBAAAAAAAAhESQCAAAAAAAACAkgkQAAAAAAAAAIREkAgAAAAAAAAiJIBEAAAAAAABASASJAAAAAAAAAEIiSAQAAAAAAAAQEkEiksSJEyds27ZtYe9fsWJFmzRpUqIeEwAAAAAAAMJHkIhEpfDw1Vdfteuvv94+/PDDSB8OEsH8+fPtqquusj59+kT6UAAAAAAAQCJKn5gvjtTp0KFDNnPmTPv2229t69attn37dtu/f3+kDyvF0bkrUqSIJXc//vij+36/+eYb69+/f6QPBwAAJDOb9h6xNxdstCkrd9i+oyctd5YMdlPlgvZwrZJWIk/WSB8eAACIBYJEJLgpU6bYtGnTrH379la7dm1r06aN/frrr5E+rBRj48aN9tJLL9nq1avtp59+suSuVatWtnz5cmvcuHGkDwUAACQjZ8+etUEz11qfH/6y02fO+rZv2nfUlmw9YK/8vM76N65oPeuXszRp0kT0WAEAQHgIEpHg7rjjDndB3IPEn3/+2apUqWIpQeXKle2zzz6L9GEAAIBkRiFi76mrfPeVFRbIltF2Hj5hZ8+aCxe9x3s1KB/BIwUAAOEiSATCcOTIEcuaNWucHj916pSlTx/+H7UMGTK468yZM1t8KgBOnjxp6dKlc5eU6vTp0yn6+AEASA1Gzltvo+dviLLt5OkztmbXEd/9bvXKWPd6Za1Izsy29cAxGzprnQ2b9bd7TGHi+EWbLEO6/5Zv71S7lHWpUzoJPwUAAAgHQSIuCCtWrLCJEyfapk2bbO3atS7YK1SokHXv3t3q1asXZb/333/f/vrrL9dCs2PHDrvooovs5ptvdlWUXuC3Z88eW7p0qdtv3rx5liVLFnvzzTd9r7N582ZbuXKle73Zs2fbFVdcYb1793aPrVq1yvfchQsXWrt27ax+/fquXVmDSQ4cOGAlS5Z0LcH33XefpU0bdeaR19oTPUD74IMP7PXXX/etN5k7d24rXry4vffee5YtWzb3vl26dHHHJvnz57e33nrLLr744jid00WLFtk777zjzpECvTNnzlilSpXs3nvvtUsvvdS337///uvOkc5HoIErx48fty+//NKdC51XnTN9frW+N23aNMq++gzLli3znbu2bdvaZZddZoMGDbI///zThbI6Bn3OunXruvU4X3nlFZszZ47t3r3bSpQo4V63RYsW5x3H0aNH3TqOc+fOtcOHD7vjyJkzp9v3gQceiFXYCwBASrbj4HFXFZhQVmw7aCu2Hwr6uELEIc2r+u4rTPTue2Gif+jovebybQfDPgb9AnXd/pPudrrth3y/mPWoErJgjkxhvx4AAAiMfzkjRVMwNWDAANde27NnT99fGhWCPfXUUy4w8igUe+ONN6xfv34umBKFegMHDrTnn3/ehUxvv/22Zc+e3bZs2eKCqx9++MEFk7feemuU91XQpXUBv/jiC9u5c2eUsFKhW44cOdxzFcItWbLEfvvtN2vevLkLuX7//XcbPny4e19NtdZxy8GDB238+PGurVm0RqLXIq7qRAWGN910k91www0uQHv00UetdevWvvdVwDZjxgx76KGH3PHr2OJa1ahz0aNHDxsyZIh7T9HnVFj68ccfuyBRAeLTTz/tzoOC22LFip0XJK5bt84efPBBF/oNHjzYMmbM6AI/HXu3bt3cNgW+0qlTJ8ubN6/bZ+rUqe79FFD+8ssv1qFDB/e9TJo0yb2/gr/nnnvOBYg6rzpP+s6HDh3qzqdCw3vuuSfKsei8XHnllfbaa6+5+zpmDYfRd6HvWJ8VAIDUYNT8DdZ/+uokeS/9flSViIFo+/DZf7s25+hGL9joLnGz87wtfRpVsH6NK8bx9QAAgCdqKRSQgigIUliksFDBkv9vnmvUqOHCvcKFC7v733//vb388ssuTFL1oUcVaQr0Lr/8chfweUHYJZdc4sIuhXYSfQHwBg0a2OOPP261atU67/GqVavajTfeaKVKlXL3169f795Dx6N1D1XR9+KLL7rHtLagquxE4eMjjzziCwdVxThhwgR3URWl3iNfvnzu+aLQMDoFjBps88wzz8SrNXrEiBFWvnx5X4goBQoUcBWenqJFi9qHH35ojz32WNDXUZirMFUBpAJC0WfQdyEKC9999133Ga+//nqrVq2a+35Kly7tC4oVNuo86zvp27evXXPNNa512wuAVdWoc67KT50/GTt27HnHognY999/v+++WtH1fAWUCk41XRwAACQsVQKqAjEQbdfjAAAg5SBIRIr1ySefuJCuUaNGAR9Xy2rZsud+Az5s2DB33bBhw4D7KoiU7777zlXzeaK3HUcXzvp9Ch2jB5GaZu2FoXv37rXYUDu0Qjm1/qqi0d9XX33lWpm9148rVUeqRdprk/Yo4Hv44YejbFMQF4hasFWtmCtXLsuUKWorkdrJ1Zqt6k1dglElY/Rzd/XVV7trfbdeNaOnZs2a7lrhZXQKLxUc+9NxlStXzt32/94BAEDCUAu11kQMRNsTssUaAAAkPlqbkWJ5LcDRwySP1taTf/75x01CFq9CMToFbwqsVOmmlma16SYUVRpG5x++eRWJ4VJF3y233GKff/65q+RT5aRHbb9q+Y2vJk2a2KeffmrNmjVz4ZxagqtXr+6qAr1Ky1Bhq9Yc1GXXrl1u7UqtX+hfOam2cgWF0cM9fwobg20LFGB6rxXTOVX4vHjxYtc6vmbNGtd+7a2tBABAatC5dilrVa1ogr3eKA1bCdKGrLZlDVbxXyPRo+2B2pqlU62S1jkWw1b0//HVa861a1coXyHgGokAACD+CBKRYnlVZ1oPLyYKsjzaV4NJolObq0IoVdEldKAUvaIueiWjhpjEVps2bVyQqNZoVQiqQlHrCar6z6vKiw+1RiuwVIXmTz/95C5eKHrXXXe5FudAn8ufzvOdd95pH330kVtLUWsRqr1YaySqhbxChQpuAE1sKz69bYECTG9gigLh6LTuoipTVQGqlm21UmtdRrVmL1iwIMQZAQDgwqGhIwk5eKRK4RxWpVD2oFObvYEqwaY2S/n8WaNMbdZrVi18/i9jgzlx4oSd3nEuPNSxeEuqAACAhEWQiBTLa5f1qg2DUbjmX42macaBeH/h9K+cSwqBQq9QKlas6AJDtTd//fXXdvvtt7tBLZ07d06wc6tKR120TuEff/zhgkoFlxpIo/ZprQMZiqojVcWoQTEaiqJ2ZAW2WntSVZVJ9Zd8Dbt54oknXEXqtGnT3FAXAACQMLrUKe0u0Q2cscZ6T13lbis01GAVVQaqndn/rz8DmlSyXg3KJ+UhAwCAOGKNRKRYGrAhM2fOjHE/teKquk405TeQ48ePu0o1rd2ngR8er+ouWNWj1jiMVMCotRJFw0pWrVrljl+twglBw1Y8Ct00+EWhoIamiFqVw6Hzp+dnyZLFDbzxgshWrVolaaWAWsB1PvW+hIgAACSNnvXLuZAwXdpzf5/SX212HPovRNR2Pa79AABAykCQiEQX0zCN+FDbrIKq//3vf771EgNRC+zdd9/tbivIUvtydKpS03H26tUrSstsnjx53LXW0otOU569lt/4iN7a7IWXoYawqDVX1ZNr1661rl27Wvv27S2hTJkyxbUIRee1hWvNxHAo4FTrtSY8e2FuJHjfefQ1F3WOQ1W0AgCAuNHfaVRpuK5XfevVoJxVK5rTSuTO4q51/9z28iGXSwEAAMkHQSISlarAvDUK1SKbkDRM5amnnnJ/+VTb6jfffBMltFSIpYsozLrmmmts37591rFjxyjhkaoUhw4dai+88MJ5U501EVrrJ2ogR8+ePd2QDgWIr776qquEVJgXrDLR2xaqajH6efEGwsydO9etK6h1CrUeYnQKPLVeoRdGNm7c2BKCXkvToF977bUo51Nt4SNHjrS2bdtGCRKPHTsWtGpT50uv9+2331qNGjXc+VRLtC5ap1DHr88Y/RzFdO689zl8+HCMn8P/uXpv+eGHH3wBqYatqDrSW3MxLmtVAgCA0ErkyWoDmlS2xd3q2YZnG7pr3dd2AACQsrBGIhKFAidV6ykk27Jli9s2ceJEW7JkiWtz1ZCN8uXjvxZOhw4dXCuy1uAbNGiQDRw40A3x0HtUqlTJPS6a3Pfmm2/aJ598Yl9++aW1bNnSSpYs6dZP1H56vu5HV7BgQRszZowNGTLEVelpYIde//7773dBWJcuXXzTklUZ+eCDD9ry5ctt9uzZ7tprE9Y6hhpg4k2S9vfII4+4ISRqUVbFX61ateyBBx5wrzlu3Dh3nvr27Rvw8+tz6PXbtWsXcDBJXGjYjKo9FZjefPPNbsCKAkVNSVZg662NuGHDBjdERQGj6LvWOoiXXnqpDRgwwG3TOoh6XOf34MGD7hIobNy5c6f7mdBnCXbuRPt476d99PkV5uocRnfrrbda8eLF3TlU+KkhLwo0FWZed911bjK1jl/rJ2qy91tvveV+VrXeJAAAAAAAOF+as3GZ9AAgQZ06dco3cTg2FN4pMNP6g6qcTG6fSa3kCpAVdKpiUusiqoJU/9lRZeE777xjr7/+uhvAsmjRokgfMsKgis6lS5e62xqkw1TMczgvwXFuguPcBMe5CYzzEhznJjjOTXCcm8A4L8FxboLj3KSec0NrM5AMxCVEFFX6KUhMbiGifPjhh9a/f39XFarKRk2C9tZA0rWOWWs7at3ChKqmBAAAAAAAiYfWZiCF0nqPX3zxhWvVTo4WLFjgrtU6HlPVoiZmN2jQIAmPDAAAAAAAxAVBIpCCaHhMxYoVXWl0nz59XLuw1nEMRusIPv/887F6jwkTJiTAkZpvzUmttVi2bNmA+2jNSq1fGWiNQwAAAAAAkLwQJAIpSOfOnV0FnyYlq1VYw0NiUrVq1QQLBmNLA2M02VoDcDRERsNNvNZmVVOq9VmDVDTMplSpUhE5RgAAAAAAED6CRCCF0NRh0eTjK664wp599lnLmzevJVeqlFTbtdZxHDt2rL3yyivueDX9OXPmzFavXj0XJOo2AAAAAABI/ggSgRQiX758rsIvJcmRI4cbqKILAAAAAABI2ZjaDAAAAAAAACAkgkQAAAAAAAAAIREkAgAAAAAAAAiJIBEAAAAAAABASASJAAAAAAAAAEIiSAQAAAAAAAAQEkEiAAAAAAAAgJAIEgEAAAAAAACERJAIAAAAAAAAICSCRAAAAAAAAAAhESQCAAAAAAAACIkgEQAAAAAAAEBIBIkAAAAAAAAAQiJIBAAAAAAAABASQSIAAAAAAACAkAgSAQAAAAAAAIREkAgAAAAAAAAgJIJEAAAAAAAAACERJAIAAAAAAAAIiSARAAAAAAAAQEgEiQAAAAAAAABCIkgEAAAAAAAAEBJBIgAAAAAAAICQCBIBAAAAAAAAhESQCAAAAAAAACAkgkQAAAAAAAAAIREkAgAAAAAAAAiJIBFAvMyfP9+uuuoq69OnT6QPBQAAAAAAJKL0ifniAC58P/74o+3fv9+++eYb69+/f6QPBwAAAKnApr1H7M0FG23Kyh227+hJy50lg91UuaA9XKuklciTNdKHBwAXLIJEAPHSqlUrW758uTVu3DjShwIAAIAL3NmzZ23QzLXW54e/7PSZs77tm/YdtSVbD9grP6+z/o0rWs/65SxNmjQRPVYAuBARJAKIl8qVK9tnn30W6cMAAABAKqAQsffUVb77ygoLZMtoOw+fsLNnzYWL3uO9GpSP4JECwIWJNRIBJFunT5+O9CEAAAAgGbUzqxLR061eGdv8XCPb1q+xu9Z9z3Pfr3L7AwASFhWJAIJatGiRvfPOO7Zjxw4X6p05c8YqVapk9957r1166aVun3///dfmzZtnK1euDDhw5fjx4/bll1/awoULbc+ePbZixQorWbKktW/f3po2bRpl31WrVtmyZcvsr7/+cvu3bdvWLrvsMhs0aJD9+eefdurUKff+Xbp0sbp169qhQ4fslVdesTlz5tju3butRIkS7nVbtGhx3nEcPXrUreM4d+5cO3z4sDuOnDlzun0feOABS5+e/xwCAAAkFzsOHndVhv5enfO3r51ZoeGQ5lV9jxXJmdl3f9isv027DfhxjT16zX/hoj9VMebOROszAMQW/3IGEJBCtx49etiQIUPspptuctt27txpvXv3to8//tjy589vTz/9tFsf8ciRI1asWLHzgsR169bZgw8+6EK/wYMHW8aMGV3g9+ijj1q3bt3ctkKFCrl9O3XqZHnz5nX7TJ061b2XAspffvnFOnToYNmzZ7dJkya591bw99xzz7kAsXnz5nbHHXe40HPo0KHWs2dPFxrec889UY7loYcesiuvvNJee+01d1/HrOEww4cPt7Vr17rPCQAAgORh1PwN1n/66oCPqZ25e72yAR/T9uGz/3Ztzm//ssldAunTqII9c33pBD1mAEgNaG0GENCIESOsfPnyvhBRChQoYN27d3e3ixYtah9++KE99thjQV/jqaeectWMCh8VEEq+fPns5ZdfdrcVFr777rs2YcIEu/76661atWp28803W+nS5/5SpwpGhY21atWySy65xPr27WvXXHONW2R74MCB9vzzz7uqxqpVq1q7du3skUcecc8bO3bsecdSpEgRu//++333s2bN6p6vgFKh6fbt2xPs3AEAACDxqJpQFYiBaLseBwAkDoJEAAEdPHjQNm/e7C7+FPI9/PDDvvsK4gLZv3+/q1bMlSuXZcqUKcpjF110keXOndu1S8e0DqIqGaNP27v66qvdddmyZX3VjJ6aNWu6a4WX0Sm8VCuzPx1XuXLl3O0tW7YEPQ4AAAAkH2p53nrgWMDHtD16SzQAIOHQ2gwgoCZNmtinn35qzZo1cwGd2oKrV6/uKgNLlSrl2y9t2sC/j9Cag7rs2rXLNm3a5NYv9GhtwwMHDrigMHq4509hY7BtgQJM77W0lmIw69evt8WLF9vq1attzZo1rv1aTp48GfQ5AAAASFqda5eyVtWKnrdG4thfNrm25aGz1kVZI9Gj7XpcHry6RIxrJAIAYo+KRAABPfPMM26oiar+fvrpJ7eG4F133eVCRd1We3FMsmXLZnfeeae7rbUUt27d6m5rjUStvVihQgV76aWXYnyNdOnSBd0WKMD0BqYEOjatu9ioUSNr1aqV/fHHH66ycdiwYXbxxRfHeAwAAABIegVzZLKqhXNEuTzbsLylS5vGN1DlyW+W+yoTda372i7arXfD8ue9hnfR6wMAYo+KRAABqe1XQ1F00VqFCt80/OSzzz6zt99+2wVwN954Y4yvoYEoqmB877333FAUtSOravCGG26wW265xbduYmL77bff7IknnrDChQvbtGnT3FAXAAAApCwl8mS1/o0rWu+pq9x9hYYarKLqQrUz+/8u+YUbK7n9AQAJiyARQNBhK48//ri7reCtfv367qI1Bfv16+falUPR+oZ6bpYsWez77793k54jQcNcVKWoakRCRAAAgJSrZ/1z61v3+eEvO33mrAsPdxz6b01EVSwqbPT2AwAkLFqbAQQ0ZcoUO3HiRMCWZdGaiaGsWrXKDWbRhGdNa44UDX4JtObi3r17bePGjRE6KgAAAMSWflHdq0F5W9ervvVqUM6qFc1p/9fevcBJVdZ/HH80LC9RSKZpglgvLguKEMGqkCsGKhcJiMryQnJpTW5BlJhZhiAoxku5lBAXTYyQEpVUjBQwUcSEkuSmBgrYIoHWuizXff6v71Nn/rM7c+bocpadZ8/n/Xpt056ZPc78OHMuv/N7fk/jBie4R/3+3+VNMybsAwDEg4pEABkqKipMSUmJmTJlihsSHPQl1EQl06dPN/37908lEvft+29fmvLy8oz1aFITresPf/iDWbFihTn55JMr9TjUMGf1KiwuLjYnnvj/Q0/27t1b6TFd8N8pKyvL+Rn0t8E627dvb5YvX26eeuopV5WoIdWabOWRRx5JvR+9TwAAAPhBw5bHdytwPwCAo4dEIoAMmsFYE6WsWbPG9OrVy9SvX98cPnzYzZSsxKJ6I27dutVNoqLkoqiPovogtm7d2owfP94tUx9EPa8eiaWlpe4nW7Jx165dbuIVDad+9tlnzauvvuqe0++PPfaYm/hF9Jrgv6fX9O3b13Tu3NkMGzYsY729e/c2jRo1MrNnz3aJT03yooSmJly5+OKL3azUev/qn7ht2zYzY8YMs2PHDtOvX78ajS0AAAAAAL4ikQgg60QrN998c87XNGnSxPUeDHPo0CGzaNEil6ibNGmSueyyy1wloIaZqF+hKgvnzJljpk6dapYuXeqShOrJGPRlzCbXf0/OPPNMs2nTpozlxx13nJspWj8fdp0AAAAAAOC/6JEIoEbMmzfPjB071gwcONBVNSo5GfSq0aOGHQ8dOtT1LQyGFwMAAAAAgPxFIhFAjXjhhRfcY4sWLXJWLe7fv9907NjxKL4zAAAAAABQHSQSAdSIs846yz2qz2KY+fPnu2HH2XocAgAAAACA/EIiEUCNGDRokJvsZMKECeaJJ55wfRED7733npk2bZp54IEHzMyZM12/RQAAAAAAkN+YbAVAjTj11FPdZCuasXnWrFluwpWGDRu6mZ+PP/54U1RU5GZk1v8HAAAAAAD5j0QigBpTv359N6GKfgAAAAAAgN+OsenjDQEANWLDhg3m4MGDxnc6ZGiSHKlXr15qJu6kIy7hiE04YhOO2GRHXMIRm3DEJhyxyY64hCM24YhNfsdGcxMUFBTEsi4qEgHgKFASsS4kEtMFB0NURlzCEZtwxCYcscmOuIQjNuGITThikx1xCUdswhGbuh0bEokAcJTuAAEAAAAA4PP1KEObAQAAAAAAAEQ6NvolAAAAAAAAAJKORCIAAAAAAACASCQSAQAAAAAAAEQikQgAAAAAAAAgEolEAAAAAAAAAJFIJAIAAAAAAACIRCIRAAAAAAAAQCQSiQAAAAAAAAAikUgEAAAAAAAAEIlEIgAAAAAAAIBIJBIBAAAAAAAARCKRCAAAAAAAACASiUQAAAAAAAAAkUgkAgAAAAAAAIhEIhEAAAAAAABAJBKJAAAAAAAAACKRSAQAAAAAAAAQiUQiAAAAAAAAgEgkEgEAAAAAAABEIpEIAAAAAAAAIBKJRAAA8sjhw4fNI488YgYOHGiGDx9u/v3vf9f2W8ob1lrzxhtv1PbbAFBHbN261Rw8eLC230ZeWb16tRk5cqS56qqrzMaNG2v77aCGHThwwJSUlNT228hLxKbm4/LPf/7TlJWVmbriQEyx2bBhg/nRj35kvv71r5tVq1aZfHSM1Vk5AAAhFi1aZH73u9+5g+Pu3bvN6aef7i4wunfv/qHW89hjj5mFCxea999/3xxzzDGmtLTUdOzY0Vx//fXmM5/5jElybALPP/+8ufXWW01BQYEZMWKE+dznPmd8FHdcZNOmTWbYsGHm0KFD5plnnjG+iiM2K1euNL/+9a/N+vXrzbvvvmsaNmxo2rdvbwYNGuS2nXz28ssvm5kzZ7r3/Z///MfUq1fP9OrVy3z72982H/3oRz/QOt5++20zdepUs3nzZlNRUeGS7RdffLHbl5x66qnGV0caGyXZ58yZ4y463nnnHXPSSSeZli1bmm9+85uma9euxmdxbDfpysvL3faiWD399NPmzDPPNEmPy1tvvWV+8pOfuPWMHj3aXHjhhcZnccRm7dq17jul2Ojvta9p0aKFGTx4sDnvvPOMz5TsWLBggXnooYdMnz593L/5h/Hee++ZadOmuRgpnaA4d+jQwX2vzj77bJPU2OzcudPMnj3brFixwq3nYx/7mPn85z9v+vbta/r16+fOf5O6zaTTsfv73/++WbJkibnvvvtMYWGh8VlJTLHReeHYsWNdInHUqFHmsssuy99tRolEAACyue222+xXvvIVW1JS4n4/cOCAnTBhgm3WrJmdOnXqB1pHRUWFHT16tP3yl79sN27cmFq+fft2e8UVV9hOnTrZd955xyYxNunuuusu27p1a/v4449bn8UdFzl06JDt06ePW0fnzp1tkmNz7733utc3b97cFhUV2Y4dO7rf9dOyZUv79NNP23z15JNP2vbt29u1a9emlml7LygosNddd537d46yZcsWW1hYaB988EG3bxHtV7Qf8XVfEkdsnn/+eXveeee57eD888933xP9bbBtTJo0yfoqju2mqnHjxqVis23bNpv0uCxZssS2adPG3nHHHfbgwYPWd3HEZv78+e6Y/NRTT6WWlZWV2VGjRrl9rb5zviktLbWPPvqoHTx4sO3Zs6eLUXX2D7t377ZdunSxkydPTm0vO3bssD169LBt27a1mzdvtkmMzaZNm9zxSX+nv7/kkktsq1atUvuakSNHpo5bSdtmqpo7d24qLqtWrbI+Ko05Ni+99JLbfnTNpH1NviORCADIavHixe6AuG7dukrLDx8+bC+//HKXyFi/fn3kenSQ1XqWLl2a8dzLL798RAkm32MT+OlPf+ouTFauXGl9FndcAjNmzHAXfz4nEuOKTd++fe2IESPszp07U8s2bNjg1hEkkfbt22fzzZtvvukuymfPnp3x3C233OLe+/33359zHYpVt27dbHFxcWh8hwwZYn0TR2wmTpzoLmTSt6Fdu3bZG264IXWxVnXbS0psqlqzZo0dMGCA14nEOOOi744SbL4dh2syNm+99ZZLACnhXFV5eblt166dveaaa6xvFixYYAcOHJg617j66qurlfjQ90f7m2zfLa2vd+/eNomxmTdvnr3ooovs6tWrKyWbgu1OP0raJ3Gbqfr96tevX+pGqK+JxAUxxubFF190+60xY8Z4k2ymRyIAICsNCWrSpIk555xzKi0/9thjTc+ePd1QFvXyi7J06VL32Lhx44znWrVqlSrlT2Js5OGHHzbz5893w3Z9H0oWZ1wCW7ZsMX/+85/dUDKfxRUbtQbQsJf0IbwaanfPPfe4de3Zs8cN6cs3Goq9b98+06NHj4znNNwwGPadi4agavhutnVceuml5vjjjzfLli1zQ+x8Ekds9Jk1lCp9aPspp5xiJk+ebM4444xK++KkxSadWgrccccdZvz48cZnccXl9ddfNz/+8Y/NBRdcYIYMGWLqgjhis3z5ctc786yzzsp4TvsZtR3x7bxF1G9t1qxZR3SuoZYazz33XNb4tm3b1jRq1Mi9xrf+mnHERsff4uJi12ok8PGPf9wds4Oh8L7th+OIS1U/+9nPXBuf6rSlqIux+de//uX60uo6SbHJ26HMVZBIBABkUD8g9aRr1qxZ1ufbtGnjHl955ZXIdamnnbzwwgsZz+3YscM9qldiEmOzd+9ed1H76U9/2gwYMMD4LM64pBs3bpw74VSSLOmxURLki1/8ovnEJz6R8ZzWHfTUzMcJenTx1KBBA3PaaadlPKfk6kc+8hEXI33GMH/84x/dY7Y46oJESTTtb9RbyCdxxEbfj2wXM+rPpf6Roj5xvokjNulmzJjh+pT52pc37rhMmDDB9Yv8wQ9+4M3F69GIjSY9CztvUW83TRDh+42/6sq1H5YgYbZu3TqTNEpgd+nSJetzutnl6344Trp5fu6556YKCWDMlClTXDLxe9/7nlfJVX/PygEANUaTGAQVLdl89rOfTSVIoqjSQX7+85+bBx980J2EiyqwdPBUA+qwE6+6Hpvf//73rmG5min7dPJQ03EJaFISVTioUbnP4oqNtpFclVRBIkCVj/lEEyupEXnY51eFz6c+9Sl38b5t27bIOCrxniuOb775pvFFXLG5/fbbzXHHHZdzu8hWXZWE2ARUzapkvRKJPosrLqoYU2WZEkKqaq4L4orN+eef7x7/9Kc/uQohVYIHNIJA6xg+fLhJoqjjWTBxkU/74bgoIR824Zev++E4qWJT53Xf/e53a/ut5FVMFi1aZD75yU+aoqIi45N6tf0GAAD5RzN+BkMysglOINNPrsNceeWV5tlnn3XDUzW8Qyfhml1WM9qpEkszKCY1NhqGGdzB14zNio2qNPW3qi7TbKu+nFjEGZf0k6u5c+ca38Udm7Dq1q1bt5qmTZvm3czNUZ8/iIFeV1ZWFvqaXbt21XgcfY1NLhpmqBlnu3XrZnwSZ2x040rJVs1K7Lu44qLhu8Hx5x//+IebnViP+jtVbF5++eXuGKQKvqTFRolVtRvR7PC/+c1vzJNPPmkGDhzo9i1qI6CbopoVPYmi9sNKsvq2Hz4atB+WK664wiTVxIkT3UzEvt84j9PKlStTo01UlagZv3WTR1XP+i5pRMF1113nboLkGyoSAQBZ7+qLLj6zCU4C1EMoil6r4WTq3yWvvfaaufHGG93wI1207Ny50yQ1NmvXrnWP9957r6tGU9Wmhn3ogldDr77zne+4fk9Ji4toyLd6xtSFE864Y5PNwoUL3d8H37N8EgzlCvv8wRBcyTXcMGo9wTqOJI6+xiaMhnn/9a9/Nf3798861DMpsVH1d7t27epENVBccQmOP7rRp/2HLvCVNNPNG637tttuc0PtkrrNDB061PW2rV+/vkse3nXXXe5YrZt9qm5NqiDGYRXQPu6Ha5oS1+rxq+R869atTRKpTYDOdZQwQ+Z+WJW+6nWtpOEDDzxgfvvb37rq3rvvvtsdv6tz/K9pVCQCQB329ttvm86dO3+ov9GwQVURpvcJqiqoUDjhhBM+0DpXrVrl7uhPnz7dVTqo8kG/L1iwwDz++OMu0Xi0Ty5qOza6W68qMhkxYoQb3hxQLHTyoEbOmiyhd+/eWfvi1cW4yOrVq90woHw74cyH2GSzfft2Vzlz/fXXp/rh5ZOgT2rY55egB+aJJ54YuR49Zkswf5B11NXYZKML+VtuucU1/ldS3jdxxUbtIzSczpebMkcrLkFlWdeuXd3NvYAmy5g2bZrr6aZ+eKqY8aWPcZzfJw19VnWQLux1DFaSVT8aDq6Y3HTTTe4CP2nS98O54lvd41ldpOHxmvRKPZ+TSEkwJcl++ctf1vZbyTu7/rcfVt9I9axNr5y+8847XYJRNwM1GZ+uCfIJiUQAqMOUxHjiiSc+1N/oLvOSJUtSF2DZ7N+/3z3qTn0UDWHWEKH77rvPfOELX3DLlBxTtd2YMWNcxYyqqNSLKFcVQV2LTfqwn2wXaRpupt5VwUnERRddZJIQF12cqDJTSed8U9uxCduObrjhBjdsNV+TRUHSL+zzf9AYaD16nSaTydYnMVhHrmGNdTU22WgYr3rS/uIXvwitHkpCbHS8UU+uulDdHGdcgkrpbMcffYe0T5k3b56rJPIlkRhXbHTT6Fvf+pa55ppr3LlK0P9Ow5uVDNINUFXNd+rUyfsevtWNcdikXkHlVHWOZ3WRbvJpqKqGwyc1Jqpy1pDuk08+ubbfSt4pzbEf1k3mPn36uH2N9sMkEgEAR40uHqtzkhv0uAmbXS5IgjVu3DgyKaQ7sWpcHiQR03sQ3X///a4ST71AlDBr2bKlSUps0u/WB0OBqjr77LNdXI7mLH+1HRdVqeozX3311RmzIYqGwmt4kKgCT5UiSYlNtgtiJUjUa1Tfs3zVsGFD95hrO1YMFN/TTz89Zxx1ga/1ZEskBj3PfBq+Gldsqpo0aZJ5/fXX3Q0cXy9e44iNWkRoWLOqnNOrPdKpT69uYmkWXh96KMa1zQTHoLDjTzALvE+zzMYVGyWftX+t2sNZ69eNLg11Vl9j9X5OWiJR+2G1pQlLJAbHM5/2wzVFFayLFy92Q1V9nym+utT3Ty0BdMxWHNIFrY1UEa0egPou5eNN5Jp0gsf7YRKJAIAMwYmxZj/MJlgelfjT5A/qJxTW5F+zlBUWFrqKLV+qReKKjT67fnQyrpOpYKbDXImoJMRFF/0a8r1ly5bQ5HTwXK6qk7oYm3S6yFUlov5GQ+zymWZT1smy/r30vrOdMCsGqsDNVZWsE2olEvXabBfvuiGhv9d6fBFXbKpWwPztb39zVSA+VWfWRGw0YVP6PiObYPbe4IItKduMhjAr0RrWp1gT1KQn55IUGw1dViIs23mJ2m506dLFJRJ9rPQ9UvqevPTSS6HbjfbDkm+Tfh1taqegCfRU1Rs2k3MSqI2RzulyzeIdbDNhybS6rFGjRu7Rx/0wk60AADKcc845riefhh1na/D797//3T327Nkz53qCHjpBL8BsVEWkO5W+3L2OKzbSoUMH96jKoWx08as+Tqo4S0pcNNmMLm6r/gT9zXShGCzzZcbvOLeZ9CSieurkexIx6JmlGwbqW7Zu3bqM5zVxgSpOoz6/KsZESbKwWTG/9KUvuQS9L+KKTXoSUa0QZs2a5XUSMa7YqH1Gtv2JfgKaBEG/awh4krYZrSOYAC2bIPnqy7DmOGOjc5eo8xZR/9GkidoPv/rqqy5R27x5c5PkJKKqETXqJslJRFF7gLB9sM7nROd3+v3RRx81SVPo8X6YRCIAIIPu1KsXh0641buwKi3TcGUlSNIbBqt/0PDhw015eblbpqohnUSpT2K2BIpmstOQMyVFfLmzH1dsghOsIHlWlZJOGuKrRu8+NC2PMy65LtxyNdFPQmyCJGLbtm1DZ1RVjNQbL5+o35homFdVmsFdVbfBMHW9dw0xveqqqyrNjqrnlVhXf7LgLn1gzZo1Zs+ePWbAgAHGN3HEJr0SUQ3tNUysKsUsbIKEuh6bXD1q8+27crTi0qtXL9OgQQNXMRS0jgjod020optdviXL4oiNkmX6Pdt2pL/RKAqNtPCp+jmbqONpMOnbX/7yl9SySy65xA0L13Gr6nFbFeO6oaNjl++qE5v0SkRVhIdVkfk8o3V145LrvM7HfXBcsSkqKjJNmjRxbRI0DLzqMVv7MT3fvXt3k3csAABZlJaW2h49ethOnTrZ9evXp5YvXLjQLSspKan0+tmzZ9tmzZq5nyVLlqSWL1++3LZq1coOHTrU7t69O7X8jTfesF/96lftnXfeaZMaG5k8ebJbPnfu3NSy7du32549e9oRI0bYgwcP2iTGpap58+a515177rl237591jdxxKa8vNwOGDDALbvwwgszfgoLC1189PyqVatsvhk9erQtKCiwixcvTi1bt26dveCCC+xzzz1XaVnw2ceOHVtpHQ899JBbPm7cOLt//363TPuVPn362KlTp1pfHWls7r77bresQ4cOGduF1tGmTRvbvHlzO2XKFOubOLabql577bXUa1988UXrozjismzZMnd8HjlypN27d69b9v7779tRo0a5/VXV/VJSYqNjsPbL3bt3txs3bkwtf/fdd+0Pf/hD279/f1tWVmZ9VlFRYbt27eo++0033ZTx/J49e1KxKS4urvTcihUrbMuWLe3w4cPd9iJ6HDx4sB0zZoz1XXVjo+OT9rPt2rUL3Q+3aNHC3njjjTZp20y2cyLFQq99+OGHre8qjiA2r7zyittmrr322tR1ks5vJk6caIuKiuzmzZttPjpG/1PbyUwAQP7OJqYqF9151tBMVcZpuMqQIUMyJjvQ8NxBgwaZk046yQ3nOOWUU1LPqWR/xowZrmpIFRBq/q+/V+VA1UlYkhYb0UzAWq4KzdNOO81VZ6pa8dJLLzVJjou2m5tvvtlVUen/B1WtmvnvjDPOMNdee+1RnWyltmOj2UKrNisPM2fOnLwbCqOqA/WLWrhwofu31GfSv6UmjGnVqlWlaihV4mpIzz333JMa+hNQhfOvfvUrV/2i74v6Kn3jG98I7cXqgyOJjSrHhg0b9oH+O5qgKF9n967p7UY0iZOqqLZv357qsaoqV/Wp0uu1v0laXDQUVfslPer7pOHBXbt2da0jfBkpUBOx0YQqM2fONM8884yLgyoZtc/u0aOHGxqtXok+0mQWy5YtcxXc6mEdaNq0qft8t99+u/v/ShGoIl69IMeOHes+dzpVP0+bNs0dmzWRiGKkqqkrr7wykbHRUPqvfe1rGdXy2Wj70aQ9SdtmRMcf9aVVr1KNvBBtO5pcUKOYVLXnk+kxxUY9JKdMmeL6j2p/pf2wKqOLi4vdeWA+IpEIAABQR+i0ThfOOjHXiSgQ0HahC3xfk0MA4LvghqgvEwwCYUgkAgAAAAAAAIjErWoAAAAAAAAAkUgkAgAAAAAAAIhEIhEAAAAAAABAJBKJAAAAAAAAACKRSAQAAAAAAAAQiUQiAAAAAAAAgEgkEgEAAAAAAABEIpEIAAAAAAAAIBKJRAAAAAAAAACRSCQCAAAAAAAAiEQiEQAAAAAAAEAkEokAAAAAAAAAIpFIBAAAAAAAABCJRCIAAAAAAACASCQSAQAAAAAAAEQikQgAAAAAAAAgEolEAAAAAAAAAJFIJAIAAAAAAACIRCIRAAAAAAAAQCQSiQAAAAAAAAAikUgEAAAAAAAAEIlEIgAAAAAAAIBIJBIBAAAAAAAARCKRCAAAAAAAAMBE+T/65WP7ODFbmwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 274,
       "width": 649
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axs = az.plot_forest(idata3sel,\n",
    "                     var_names=[\"Intercept\", \"1|county\", \"floor\", \"1|county_sigma\", \"sigma\"],\n",
    "                     combined=True, figsize=(6,3))\n",
    "axs[0].set_xticks(np.arange(-0.8,1.6,0.2))\n",
    "axs[0].set_title(None);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "96a9d401-70fb-43f7-a381-4ad5f47ef913",
   "metadata": {},
   "outputs": [],
   "source": [
    "# az.plot_forest(idata3, var_names=[\"1|county\"], combined=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "463cc228-67ea-4045-b6ab-baca65132b47",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "### Compare models\n",
    "\n",
    "Compare complete pooling,  no pooling, and partial pooling models\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "833f1851-1c68-4442-bc7f-9f4bb9c00228",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x400 with 8 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 369,
       "width": 848
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_counties(radon, idata_cp=idata1, idata_np=idata2, idata_pp=idata3);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "174ff010-1111-45d2-a9b1-682972793aa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Forest plot comparing `mod2` and `mod3` estimates\n",
    "# # (to illustrate reduced uncertainty in estimates + shrinkage)\n",
    "# post3 = idata3sel[\"posterior\"]\n",
    "# post3[\"county\"] = post3[\"Intercept\"] + post3[\"1|county\"]\n",
    "# axs = az.plot_forest([idata2sel, idata3sel], model_names=[\"mod2\", \"mod3\"],\n",
    "#                      var_names=[\"county\", \"floor\", \"1|county_sigma\", \"sigma\"], combined=True, figsize=(6,3))\n",
    "# ax = axs[0]\n",
    "# rbar = radon[\"log_radon\"].mean()\n",
    "# ax.axvline(rbar, ls=\"--\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50171a57-21f2-408d-9c05-5003a1ee7630",
   "metadata": {},
   "source": [
    "### Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1008c69e-fb81-416e-b218-7f7480571c70",
   "metadata": {},
   "source": [
    "## Explanations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e531158d-0d2b-4d4c-bf6c-54126a068ce2",
   "metadata": {},
   "source": [
    "### Prior selection for hierarchical models\n",
    "\n",
    "?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ec4237b-1542-44c4-85c0-63ff68db797b",
   "metadata": {},
   "source": [
    "### Varying intercepts and slopes model\n",
    "\n",
    "= Group-specific slopes\n",
    "We can also make beta_x group-specific\n",
    "\n",
    "\n",
    "The varying-slope, varying intercept model adds `floor` to the\n",
    "group-level co-efficients. Now estimates across counties will all have\n",
    "varying slope.\n",
    "\n",
    "    log_radon ~ 1 + floor + (1 + floor | county)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6cbd7127-47f8-4e03-879c-89a2f1772617",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       Formula: log_radon ~ 1 + (1|county) + floor + (floor|county)\n",
       "        Family: gaussian\n",
       "          Link: mu = identity\n",
       "  Observations: 919\n",
       "        Priors: \n",
       "    target = mu\n",
       "        Common-level effects\n",
       "            Intercept ~ Normal(mu: 1.0, sigma: 2.0)\n",
       "            floor ~ Normal(mu: -1.0, sigma: 2.0)\n",
       "        \n",
       "        Group-level effects\n",
       "            1|county ~ Normal(mu: 0.0, sigma: Exponential(lam: 1.0))\n",
       "            floor|county ~ Normal(mu: 0.0, sigma: Exponential(lam: 1.0))\n",
       "        \n",
       "        Auxiliary parameters\n",
       "            sigma ~ HalfStudentT(nu: 4.0, sigma: 1.0)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#######################################################\n",
    "formula4 = \"log_radon ~ 1 + (1|county) + floor + (floor|county)\"\n",
    "\n",
    "SigmaB0j = bmb.Prior(\"Exponential\", lam=1)\n",
    "SigmaBfj = bmb.Prior(\"Exponential\", lam=1)\n",
    "\n",
    "priors4 = {\n",
    "    \"Intercept\": bmb.Prior(\"Normal\", mu=1, sigma=2),\n",
    "    \"1|county\": bmb.Prior(\"Normal\", mu=0, sigma=SigmaB0j),\n",
    "    \"floor\": bmb.Prior(\"Normal\", mu=-1, sigma=2),\n",
    "    \"floor|county\": bmb.Prior(\"Normal\", mu=0, sigma=SigmaBfj),\n",
    "    \"sigma\": bmb.Prior(\"HalfStudentT\", nu=4, sigma=1),\n",
    "}\n",
    "\n",
    "mod4 = bmb.Model(formula=formula4,\n",
    "                 family=\"gaussian\",\n",
    "                 link=\"identity\",\n",
    "                 priors=priors4,\n",
    "                 data=radon,\n",
    "                 noncentered=False)\n",
    "mod4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "13fa5013-9104-4772-ae85-64968a4e2d37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 12.1.0 (20240811.2233)\n",
       " -->\n",
       "<!-- Pages: 1 -->\n",
       "<svg width=\"661pt\" height=\"515pt\"\n",
       " viewBox=\"0.00 0.00 661.25 514.77\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 510.77)\">\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-510.77 657.25,-510.77 657.25,4 -4,4\"/>\n",
       "<g id=\"clust1\" class=\"cluster\">\n",
       "<title>clusterfloor_dim (1)</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M112.25,-247.13C112.25,-247.13 186.25,-247.13 186.25,-247.13 192.25,-247.13 198.25,-253.13 198.25,-259.13 198.25,-259.13 198.25,-356.95 198.25,-356.95 198.25,-362.95 192.25,-368.95 186.25,-368.95 186.25,-368.95 112.25,-368.95 112.25,-368.95 106.25,-368.95 100.25,-362.95 100.25,-356.95 100.25,-356.95 100.25,-259.13 100.25,-259.13 100.25,-253.13 106.25,-247.13 112.25,-247.13\"/>\n",
       "<text text-anchor=\"middle\" x=\"153.13\" y=\"-254.33\" font-family=\"Times,serif\" font-size=\"14.00\">floor_dim (1)</text>\n",
       "</g>\n",
       "<g id=\"clust2\" class=\"cluster\">\n",
       "<title>clustercounty__factor_dim (85)</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M218.25,-247.13C218.25,-247.13 346.25,-247.13 346.25,-247.13 352.25,-247.13 358.25,-253.13 358.25,-259.13 358.25,-259.13 358.25,-356.95 358.25,-356.95 358.25,-362.95 352.25,-368.95 346.25,-368.95 346.25,-368.95 218.25,-368.95 218.25,-368.95 212.25,-368.95 206.25,-362.95 206.25,-356.95 206.25,-356.95 206.25,-259.13 206.25,-259.13 206.25,-253.13 212.25,-247.13 218.25,-247.13\"/>\n",
       "<text text-anchor=\"middle\" x=\"282\" y=\"-254.33\" font-family=\"Times,serif\" font-size=\"14.00\">county__factor_dim (85)</text>\n",
       "</g>\n",
       "<g id=\"clust3\" class=\"cluster\">\n",
       "<title>clusterfloor__expr_dim (1)</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M351.25,-376.95C351.25,-376.95 515.25,-376.95 515.25,-376.95 521.25,-376.95 527.25,-382.95 527.25,-388.95 527.25,-388.95 527.25,-486.77 527.25,-486.77 527.25,-492.77 521.25,-498.77 515.25,-498.77 515.25,-498.77 351.25,-498.77 351.25,-498.77 345.25,-498.77 339.25,-492.77 339.25,-486.77 339.25,-486.77 339.25,-388.95 339.25,-388.95 339.25,-382.95 345.25,-376.95 351.25,-376.95\"/>\n",
       "<text text-anchor=\"middle\" x=\"463.38\" y=\"-384.15\" font-family=\"Times,serif\" font-size=\"14.00\">floor__expr_dim (1)</text>\n",
       "</g>\n",
       "<g id=\"clust4\" class=\"cluster\">\n",
       "<title>clustercounty__factor_dim (85) x floor__expr_dim (1)</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M379.25,-247.13C379.25,-247.13 633.25,-247.13 633.25,-247.13 639.25,-247.13 645.25,-253.13 645.25,-259.13 645.25,-259.13 645.25,-356.95 645.25,-356.95 645.25,-362.95 639.25,-368.95 633.25,-368.95 633.25,-368.95 379.25,-368.95 379.25,-368.95 373.25,-368.95 367.25,-362.95 367.25,-356.95 367.25,-356.95 367.25,-259.13 367.25,-259.13 367.25,-253.13 373.25,-247.13 379.25,-247.13\"/>\n",
       "<text text-anchor=\"middle\" x=\"506\" y=\"-254.33\" font-family=\"Times,serif\" font-size=\"14.00\">county__factor_dim (85) x floor__expr_dim (1)</text>\n",
       "</g>\n",
       "<g id=\"clust5\" class=\"cluster\">\n",
       "<title>cluster__obs__ (919)</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M160.25,-8C160.25,-8 252.25,-8 252.25,-8 258.25,-8 264.25,-14 264.25,-20 264.25,-20 264.25,-223.23 264.25,-223.23 264.25,-229.23 258.25,-235.23 252.25,-235.23 252.25,-235.23 160.25,-235.23 160.25,-235.23 154.25,-235.23 148.25,-229.23 148.25,-223.23 148.25,-223.23 148.25,-20 148.25,-20 148.25,-14 154.25,-8 160.25,-8\"/>\n",
       "<text text-anchor=\"middle\" x=\"216.88\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">__obs__ (919)</text>\n",
       "</g>\n",
       "<!-- sigma -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>sigma</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"77.25\" cy=\"-198.48\" rx=\"63.29\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"77.25\" y=\"-209.93\" font-family=\"Times,serif\" font-size=\"14.00\">sigma</text>\n",
       "<text text-anchor=\"middle\" x=\"77.25\" y=\"-193.43\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"77.25\" y=\"-176.93\" font-family=\"Times,serif\" font-size=\"14.00\">HalfStudentT</text>\n",
       "</g>\n",
       "<!-- log_radon -->\n",
       "<g id=\"node8\" class=\"node\">\n",
       "<title>log_radon</title>\n",
       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"206.25\" cy=\"-81.16\" rx=\"50.03\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"206.25\" y=\"-92.61\" font-family=\"Times,serif\" font-size=\"14.00\">log_radon</text>\n",
       "<text text-anchor=\"middle\" x=\"206.25\" y=\"-76.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"206.25\" y=\"-59.61\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- sigma&#45;&gt;log_radon -->\n",
       "<g id=\"edge7\" class=\"edge\">\n",
       "<title>sigma&#45;&gt;log_radon</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M113.53,-165.05C129.4,-150.86 148.1,-134.15 164.54,-119.45\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"166.65,-122.26 171.77,-112.99 161.98,-117.04 166.65,-122.26\"/>\n",
       "</g>\n",
       "<!-- 1|county_sigma -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>1|county_sigma</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"257.25\" cy=\"-450.11\" rx=\"72.3\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"257.25\" y=\"-461.56\" font-family=\"Times,serif\" font-size=\"14.00\">1|county_sigma</text>\n",
       "<text text-anchor=\"middle\" x=\"257.25\" y=\"-445.06\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"257.25\" y=\"-428.56\" font-family=\"Times,serif\" font-size=\"14.00\">Exponential</text>\n",
       "</g>\n",
       "<!-- 1|county -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>1|county</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"258.25\" cy=\"-320.29\" rx=\"44.19\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"258.25\" y=\"-331.74\" font-family=\"Times,serif\" font-size=\"14.00\">1|county</text>\n",
       "<text text-anchor=\"middle\" x=\"258.25\" y=\"-315.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"258.25\" y=\"-298.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- 1|county_sigma&#45;&gt;1|county -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>1|county_sigma&#45;&gt;1|county</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M257.57,-409.02C257.66,-397.48 257.76,-384.73 257.85,-372.61\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"261.35,-372.78 257.93,-362.76 254.35,-372.73 261.35,-372.78\"/>\n",
       "</g>\n",
       "<!-- Intercept -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>Intercept</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"45.25\" cy=\"-320.29\" rx=\"45.25\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"45.25\" y=\"-331.74\" font-family=\"Times,serif\" font-size=\"14.00\">Intercept</text>\n",
       "<text text-anchor=\"middle\" x=\"45.25\" y=\"-315.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"45.25\" y=\"-298.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- mu -->\n",
       "<g id=\"node9\" class=\"node\">\n",
       "<title>mu</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"251.38,-227.23 161.13,-227.23 161.13,-169.73 251.38,-169.73 251.38,-227.23\"/>\n",
       "<text text-anchor=\"middle\" x=\"206.25\" y=\"-209.93\" font-family=\"Times,serif\" font-size=\"14.00\">mu</text>\n",
       "<text text-anchor=\"middle\" x=\"206.25\" y=\"-193.43\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"206.25\" y=\"-176.93\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
       "</g>\n",
       "<!-- Intercept&#45;&gt;mu -->\n",
       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>Intercept&#45;&gt;mu</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M63.01,-282.8C71.29,-269.51 82.41,-255.67 96.25,-247.13 116.53,-234.63 127.12,-247.95 149.25,-239.13 153.3,-237.53 157.33,-235.58 161.28,-233.43\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"162.77,-236.61 169.58,-228.5 159.2,-230.6 162.77,-236.61\"/>\n",
       "</g>\n",
       "<!-- floor -->\n",
       "<g id=\"node4\" class=\"node\">\n",
       "<title>floor</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"149.25\" cy=\"-320.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"149.25\" y=\"-331.74\" font-family=\"Times,serif\" font-size=\"14.00\">floor</text>\n",
       "<text text-anchor=\"middle\" x=\"149.25\" y=\"-315.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"149.25\" y=\"-298.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- floor&#45;&gt;mu -->\n",
       "<g id=\"edge5\" class=\"edge\">\n",
       "<title>floor&#45;&gt;mu</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M166.51,-283.02C173.31,-268.72 181.13,-252.28 188.03,-237.79\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"191.07,-239.54 192.21,-229 184.75,-236.53 191.07,-239.54\"/>\n",
       "</g>\n",
       "<!-- 1|county&#45;&gt;mu -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>1|county&#45;&gt;mu</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M242.09,-282.04C236,-268.02 229.08,-252.07 222.95,-237.95\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"226.24,-236.74 219.05,-228.96 219.82,-239.52 226.24,-236.74\"/>\n",
       "</g>\n",
       "<!-- floor|county_sigma -->\n",
       "<g id=\"node6\" class=\"node\">\n",
       "<title>floor|county_sigma</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"433.25\" cy=\"-450.11\" rx=\"86.09\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"433.25\" y=\"-461.56\" font-family=\"Times,serif\" font-size=\"14.00\">floor|county_sigma</text>\n",
       "<text text-anchor=\"middle\" x=\"433.25\" y=\"-445.06\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"433.25\" y=\"-428.56\" font-family=\"Times,serif\" font-size=\"14.00\">Exponential</text>\n",
       "</g>\n",
       "<!-- floor|county -->\n",
       "<g id=\"node7\" class=\"node\">\n",
       "<title>floor|county</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"433.25\" cy=\"-320.29\" rx=\"57.98\" ry=\"40.66\"/>\n",
       "<text text-anchor=\"middle\" x=\"433.25\" y=\"-331.74\" font-family=\"Times,serif\" font-size=\"14.00\">floor|county</text>\n",
       "<text text-anchor=\"middle\" x=\"433.25\" y=\"-315.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"433.25\" y=\"-298.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- floor|county_sigma&#45;&gt;floor|county -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>floor|county_sigma&#45;&gt;floor|county</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M433.25,-409.02C433.25,-397.48 433.25,-384.73 433.25,-372.61\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"436.75,-372.76 433.25,-362.76 429.75,-372.76 436.75,-372.76\"/>\n",
       "</g>\n",
       "<!-- floor|county&#45;&gt;mu -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>floor|county&#45;&gt;mu</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M405.44,-284.32C393.38,-271.09 378.34,-256.88 362.25,-247.13 331.84,-228.7 293.73,-216.88 262.79,-209.63\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"263.74,-206.25 253.22,-207.49 262.21,-213.08 263.74,-206.25\"/>\n",
       "</g>\n",
       "<!-- mu&#45;&gt;log_radon -->\n",
       "<g id=\"edge8\" class=\"edge\">\n",
       "<title>mu&#45;&gt;log_radon</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M206.25,-169.27C206.25,-158.44 206.25,-145.73 206.25,-133.41\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"209.75,-133.78 206.25,-123.78 202.75,-133.78 209.75,-133.78\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.graphs.Digraph at 0x13abf8e30>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod4.build()\n",
    "mod4.graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f5df25ff-a39b-4204-844b-8ac09bb8872e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [sigma, Intercept, floor, 1|county_sigma, 1|county, floor|county_sigma, floor|county]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e2646017982c4acf9a73784c533f4386",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 3_000 tune and 2_000 draw iterations (12_000 + 8_000 draws total) took 17 seconds.\n",
      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
     ]
    }
   ],
   "source": [
    "idata4 = mod4.fit(draws=2000, tune=3000, random_seed=42, target_accept=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e8eea874-3f5b-4190-8f40-9dea36c741ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# az.autocorr(idata4[\"posterior\"][\"sigma\"].values.flatten())[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ce49b23f-7dd4-42fc-96c8-98ddcb10a808",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>0.750</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.716</td>\n",
       "      <td>0.785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>1.458</td>\n",
       "      <td>0.053</td>\n",
       "      <td>1.360</td>\n",
       "      <td>1.560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floor[ground]</th>\n",
       "      <td>-0.676</td>\n",
       "      <td>0.085</td>\n",
       "      <td>-0.835</td>\n",
       "      <td>-0.514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|county_sigma</th>\n",
       "      <td>0.336</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.249</td>\n",
       "      <td>0.422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|county[LAC QUI PARLE]</th>\n",
       "      <td>0.399</td>\n",
       "      <td>0.296</td>\n",
       "      <td>-0.156</td>\n",
       "      <td>0.948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|county[AITKIN]</th>\n",
       "      <td>-0.283</td>\n",
       "      <td>0.259</td>\n",
       "      <td>-0.777</td>\n",
       "      <td>0.191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|county[KOOCHICHING]</th>\n",
       "      <td>-0.396</td>\n",
       "      <td>0.243</td>\n",
       "      <td>-0.837</td>\n",
       "      <td>0.081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|county[DOUGLAS]</th>\n",
       "      <td>0.168</td>\n",
       "      <td>0.204</td>\n",
       "      <td>-0.217</td>\n",
       "      <td>0.543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|county[HENNEPIN]</th>\n",
       "      <td>-0.089</td>\n",
       "      <td>0.090</td>\n",
       "      <td>-0.259</td>\n",
       "      <td>0.078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|county[STEARNS]</th>\n",
       "      <td>0.034</td>\n",
       "      <td>0.149</td>\n",
       "      <td>-0.253</td>\n",
       "      <td>0.309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|county[RAMSEY]</th>\n",
       "      <td>-0.280</td>\n",
       "      <td>0.133</td>\n",
       "      <td>-0.523</td>\n",
       "      <td>-0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|county[ST LOUIS]</th>\n",
       "      <td>-0.576</td>\n",
       "      <td>0.088</td>\n",
       "      <td>-0.745</td>\n",
       "      <td>-0.413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floor|county_sigma[ground]</th>\n",
       "      <td>0.274</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floor|county[ground, LAC QUI PARLE]</th>\n",
       "      <td>0.161</td>\n",
       "      <td>0.296</td>\n",
       "      <td>-0.363</td>\n",
       "      <td>0.748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floor|county[ground, AITKIN]</th>\n",
       "      <td>0.038</td>\n",
       "      <td>0.271</td>\n",
       "      <td>-0.477</td>\n",
       "      <td>0.597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floor|county[ground, KOOCHICHING]</th>\n",
       "      <td>0.036</td>\n",
       "      <td>0.241</td>\n",
       "      <td>-0.424</td>\n",
       "      <td>0.519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floor|county[ground, DOUGLAS]</th>\n",
       "      <td>0.048</td>\n",
       "      <td>0.270</td>\n",
       "      <td>-0.473</td>\n",
       "      <td>0.586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floor|county[ground, HENNEPIN]</th>\n",
       "      <td>-0.092</td>\n",
       "      <td>0.186</td>\n",
       "      <td>-0.467</td>\n",
       "      <td>0.248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floor|county[ground, STEARNS]</th>\n",
       "      <td>-0.096</td>\n",
       "      <td>0.234</td>\n",
       "      <td>-0.567</td>\n",
       "      <td>0.338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floor|county[ground, RAMSEY]</th>\n",
       "      <td>0.218</td>\n",
       "      <td>0.263</td>\n",
       "      <td>-0.258</td>\n",
       "      <td>0.710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floor|county[ground, ST LOUIS]</th>\n",
       "      <td>0.045</td>\n",
       "      <td>0.164</td>\n",
       "      <td>-0.256</td>\n",
       "      <td>0.375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      mean     sd  hdi_3%  hdi_97%\n",
       "sigma                                0.750  0.018   0.716    0.785\n",
       "Intercept                            1.458  0.053   1.360    1.560\n",
       "floor[ground]                       -0.676  0.085  -0.835   -0.514\n",
       "1|county_sigma                       0.336  0.046   0.249    0.422\n",
       "1|county[LAC QUI PARLE]              0.399  0.296  -0.156    0.948\n",
       "1|county[AITKIN]                    -0.283  0.259  -0.777    0.191\n",
       "1|county[KOOCHICHING]               -0.396  0.243  -0.837    0.081\n",
       "1|county[DOUGLAS]                    0.168  0.204  -0.217    0.543\n",
       "1|county[HENNEPIN]                  -0.089  0.090  -0.259    0.078\n",
       "1|county[STEARNS]                    0.034  0.149  -0.253    0.309\n",
       "1|county[RAMSEY]                    -0.280  0.133  -0.523   -0.025\n",
       "1|county[ST LOUIS]                  -0.576  0.088  -0.745   -0.413\n",
       "floor|county_sigma[ground]           0.274  0.119   0.057    0.469\n",
       "floor|county[ground, LAC QUI PARLE]  0.161  0.296  -0.363    0.748\n",
       "floor|county[ground, AITKIN]         0.038  0.271  -0.477    0.597\n",
       "floor|county[ground, KOOCHICHING]    0.036  0.241  -0.424    0.519\n",
       "floor|county[ground, DOUGLAS]        0.048  0.270  -0.473    0.586\n",
       "floor|county[ground, HENNEPIN]      -0.092  0.186  -0.467    0.248\n",
       "floor|county[ground, STEARNS]       -0.096  0.234  -0.567    0.338\n",
       "floor|county[ground, RAMSEY]         0.218  0.263  -0.258    0.710\n",
       "floor|county[ground, ST LOUIS]       0.045  0.164  -0.256    0.375"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idata4sel = idata4.sel(county__factor_dim=sel_counties)\n",
    "az.summary(idata4sel, kind=\"stats\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "be8ea35f-42e5-4b91-9808-ee88817c7ca5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.716</td>\n",
       "      <td>0.785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean     sd  hdi_3%  hdi_97%\n",
       "sigma  0.75  0.018   0.716    0.785"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(idata4, var_names=\"sigma\", kind=\"stats\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6ee762b-c74f-4d24-8f43-8a4c8a313ea5",
   "metadata": {},
   "source": [
    "The estimated mean standard deviation is the lowest of all the models we considered so far."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f4138877-c5af-43b0-9360-5b2194907732",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x400 with 8 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 369,
       "width": 848
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_counties(radon, idata_cp=idata1, idata_np=idata2, idata_pp=idata3, idata_pp2=idata4);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ed84a73a-4274-4b4d-a001-4135afba6a4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 351,
       "width": 668
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "idata4sel = idata4.sel(county__factor_dim=sel_counties)\n",
    "var_names = [\"Intercept\",\n",
    "             \"1|county\",\n",
    "             \"floor\",\n",
    "             \"floor|county\",\n",
    "             \"1|county_sigma\",\n",
    "             \"floor|county_sigma\",\n",
    "             \"sigma\"]\n",
    "axs = az.plot_forest(idata4sel, combined=True, var_names=var_names, figsize=(6,4))\n",
    "axs[0].set_xticks(np.arange(-1.6,1.6,0.2))\n",
    "axs[0].set_title(None);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adf6e688-1928-4586-a221-3a1dd4347a38",
   "metadata": {},
   "source": [
    "### Comparing models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ae224769-d6fd-4386-86e1-1c417bc2abdd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
     ]
    }
   ],
   "source": [
    "from ministats.utils import loglevel\n",
    "with loglevel(\"ERROR\", module=\"pymc\"):\n",
    "    idata1ll = mod1.fit(idata_kwargs={\"log_likelihood\": True}, random_seed=42, progressbar=False)\n",
    "    idata2ll = mod2.fit(idata_kwargs={\"log_likelihood\": True}, random_seed=42, progressbar=False)\n",
    "    idata3ll = mod3.fit(idata_kwargs={\"log_likelihood\": True}, random_seed=42, progressbar=False)\n",
    "    idata4ll = mod4.fit(idata_kwargs={\"log_likelihood\": True}, random_seed=42, progressbar=False,\n",
    "                        draws=2000, tune=3000, target_accept=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a3df3c7-cc40-4eb9-a023-45ea79ff8824",
   "metadata": {},
   "source": [
    "Compare models based on their expected log pointwise predictive density (ELPD).\n",
    "\n",
    "The ELPD is estimated either by Pareto smoothed importance sampling leave-one-out cross-validation (LOO) or using the widely applicable information criterion (WAIC). We recommend loo. Read more theory here - in a paper by some of the leading authorities on model comparison dx.doi.org/10.1111/1467-9868.00353"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1ae18d08-607d-4194-8c08-3dc62f5fb315",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>elpd_loo</th>\n",
       "      <th>p_loo</th>\n",
       "      <th>elpd_diff</th>\n",
       "      <th>weight</th>\n",
       "      <th>se</th>\n",
       "      <th>dse</th>\n",
       "      <th>warning</th>\n",
       "      <th>scale</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mod3 (varying intercepts)</th>\n",
       "      <td>0</td>\n",
       "      <td>-1074.082946</td>\n",
       "      <td>49.438388</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.712644e-01</td>\n",
       "      <td>28.158630</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>log</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mod4 (varying int. and slopes)</th>\n",
       "      <td>1</td>\n",
       "      <td>-1075.845532</td>\n",
       "      <td>65.772502</td>\n",
       "      <td>1.762586</td>\n",
       "      <td>3.492005e-01</td>\n",
       "      <td>28.887361</td>\n",
       "      <td>2.381882</td>\n",
       "      <td>True</td>\n",
       "      <td>log</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mod2 (no pooling)</th>\n",
       "      <td>2</td>\n",
       "      <td>-1096.122253</td>\n",
       "      <td>83.726370</td>\n",
       "      <td>22.039307</td>\n",
       "      <td>2.500135e-18</td>\n",
       "      <td>28.240890</td>\n",
       "      <td>6.110614</td>\n",
       "      <td>True</td>\n",
       "      <td>log</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mod1 (complete pooling)</th>\n",
       "      <td>3</td>\n",
       "      <td>-1126.954906</td>\n",
       "      <td>3.813040</td>\n",
       "      <td>52.871960</td>\n",
       "      <td>7.953513e-02</td>\n",
       "      <td>25.538335</td>\n",
       "      <td>10.607278</td>\n",
       "      <td>False</td>\n",
       "      <td>log</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                rank     elpd_loo      p_loo  elpd_diff  \\\n",
       "mod3 (varying intercepts)          0 -1074.082946  49.438388   0.000000   \n",
       "mod4 (varying int. and slopes)     1 -1075.845532  65.772502   1.762586   \n",
       "mod2 (no pooling)                  2 -1096.122253  83.726370  22.039307   \n",
       "mod1 (complete pooling)            3 -1126.954906   3.813040  52.871960   \n",
       "\n",
       "                                      weight         se        dse  warning  \\\n",
       "mod3 (varying intercepts)       5.712644e-01  28.158630   0.000000    False   \n",
       "mod4 (varying int. and slopes)  3.492005e-01  28.887361   2.381882     True   \n",
       "mod2 (no pooling)               2.500135e-18  28.240890   6.110614     True   \n",
       "mod1 (complete pooling)         7.953513e-02  25.538335  10.607278    False   \n",
       "\n",
       "                               scale  \n",
       "mod3 (varying intercepts)        log  \n",
       "mod4 (varying int. and slopes)   log  \n",
       "mod2 (no pooling)                log  \n",
       "mod1 (complete pooling)          log  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "radon_models = {\n",
    "    \"mod1 (complete pooling)\": idata1ll,\n",
    "    \"mod2 (no pooling)\": idata2ll,\n",
    "    \"mod3 (varying intercepts)\": idata3ll,\n",
    "    \"mod4 (varying int. and slopes)\": idata4ll,\n",
    "}\n",
    "compare_results = az.compare(radon_models)\n",
    "compare_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "75a86ace-9576-43a2-9f5f-46b0e7aa7a93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 419,
       "width": 728
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_compare(compare_results);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99439024-a76f-4ab2-bcff-2b82b530a11d",
   "metadata": {},
   "source": [
    "#### ELPD and elpd_loo\n",
    "\n",
    "The ELPD is the theoretical expected log pointwise predictive density for a new dataset (Eq 1 in VGG2017), which can be estimated, e.g., using cross-validation. elpd_loo is the Bayesian LOO estimate of the expected log pointwise predictive density (Eq 4 in VGG2017) and is a sum of N individual pointwise log predictive densities. Probability densities can be smaller or larger than 1, and thus log predictive densities can be negative or positive. For simplicity the ELPD acronym is used also for expected log pointwise predictive probabilities for discrete models. Probabilities are always equal or less than 1, and thus log predictive probabilities are 0 or negative.\n",
    "\n",
    "via https://mc-stan.org/loo/reference/loo-glossary.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f240893d-d3e7-420b-a244-b7c256714f1c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "344f4faf-796b-48ec-8d3b-f2efd77f1241",
   "metadata": {},
   "source": [
    "### Frequentist multilevel models\n",
    "\n",
    "We can use `statsmodels` to fit multilevel models too."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83cd9ab0-cb32-46d7-812b-9f6577dbc7be",
   "metadata": {},
   "source": [
    "#### Random intercepts model using `statsmodels`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "35392a05-2956-4581-95e8-e4d5cf5be81b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coef.</th>\n",
       "      <th>Std.Err.</th>\n",
       "      <th>z</th>\n",
       "      <th>P&gt;|z|</th>\n",
       "      <th>[0.025</th>\n",
       "      <th>0.975]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>1.462</td>\n",
       "      <td>0.052</td>\n",
       "      <td>28.164</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.360</td>\n",
       "      <td>1.563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floor[T.ground]</th>\n",
       "      <td>-0.693</td>\n",
       "      <td>0.071</td>\n",
       "      <td>-9.818</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.831</td>\n",
       "      <td>-0.555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>county Var</th>\n",
       "      <td>0.108</td>\n",
       "      <td>0.041</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Coef. Std.Err.       z  P>|z|  [0.025  0.975]\n",
       "Intercept         1.462    0.052  28.164  0.000   1.360   1.563\n",
       "floor[T.ground]  -0.693    0.071  -9.818  0.000  -0.831  -0.555\n",
       "county Var        0.108    0.041                               "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "sm3 = smf.mixedlm(\"log_radon ~ 1 + floor\",   # Fixed effects (no intercept and floor as a fixed effect)\n",
    "                  groups=\"county\",           # Grouping variable for random effects\n",
    "                  re_formula=\"1\",            # Random effects = intercept\n",
    "                  data=radon)\n",
    "res3 = sm3.fit()\n",
    "res3.summary().tables[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2ac6d8e5-7d8c-4df6-b9f2-28af8453c0d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.6929937406558051"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# slope\n",
    "#######################################################\n",
    "res3.params[\"floor[T.ground]\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8c10c023-78d5-42f2-b8fe-7975d75b0e12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7555891484188185"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sigma-hat\n",
    "np.sqrt(res3.scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "afbe9fe2-fdb7-429e-8598-751c5b8b552c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>county</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>county</th>\n",
       "      <td>0.328222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          county\n",
       "county  0.328222"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# standard deviation of the variability among county-specific Intercepts\n",
    "np.sqrt(res3.cov_re)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "826007cf-4764-415d-99dd-c73d52cc4389",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.40649212])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# intercept deviation for first country in res3\n",
    "res3.random_effects[\"LAC QUI PARLE\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "da58b8e2-6134-4083-8778-a9ed9dcd76c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.41388794)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compare with corresponding Bayesian point estimate\n",
    "lqp = idata3.sel(county__factor_dim=\"LAC QUI PARLE\")\n",
    "post3_lqp_means = lqp[\"posterior\"].mean()\n",
    "post3_lqp_means[\"1|county\"].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53c041b8-7efb-4238-a19c-0163279421a9",
   "metadata": {},
   "source": [
    "This is very close to the mean of the random effect coefficient for `AITKIN` in the Bayesian model `mod3` which was $-0.268$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b302def9-f8bc-4cf9-981a-602c730100a2",
   "metadata": {},
   "source": [
    "#### Random intercepts and slopes model using `statsmodels` (BONUS TOPIC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "349d79b6-5252-4d9d-ae1f-a59b0532308b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coef.</th>\n",
       "      <th>Std.Err.</th>\n",
       "      <th>z</th>\n",
       "      <th>P&gt;|z|</th>\n",
       "      <th>[0.025</th>\n",
       "      <th>0.975]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>1.463</td>\n",
       "      <td>0.054</td>\n",
       "      <td>26.977</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.356</td>\n",
       "      <td>1.569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floor[T.ground]</th>\n",
       "      <td>-0.681</td>\n",
       "      <td>0.089</td>\n",
       "      <td>-7.624</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.856</td>\n",
       "      <td>-0.506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>county Var</th>\n",
       "      <td>0.122</td>\n",
       "      <td>0.049</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>county x floor[T.ground] Cov</th>\n",
       "      <td>-0.040</td>\n",
       "      <td>0.057</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floor[T.ground] Var</th>\n",
       "      <td>0.118</td>\n",
       "      <td>0.120</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Coef. Std.Err.       z  P>|z|  [0.025  0.975]\n",
       "Intercept                      1.463    0.054  26.977  0.000   1.356   1.569\n",
       "floor[T.ground]               -0.681    0.089  -7.624  0.000  -0.856  -0.506\n",
       "county Var                     0.122    0.049                               \n",
       "county x floor[T.ground] Cov  -0.040    0.057                               \n",
       "floor[T.ground] Var            0.118    0.120                               "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm4 = smf.mixedlm(\"log_radon ~ 1 + floor\",   # Fixed effects (no intercept and floor as a fixed effect)\n",
    "                  groups=\"county\",           # Grouping variable for random effects\n",
    "                  re_formula=\"1 + floor\",    # Random effects: 1 for intercept, floor for slope\n",
    "                  data=radon)\n",
    "res4 = sm4.fit()\n",
    "res4.summary().tables[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c43041d8-69f6-4377-b2c7-82832408daf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.6810977572101939"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# slope estimate\n",
    "res4.params[\"floor[T.ground]\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7af23283-bcda-4d5e-b771-a073089e5110",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7461559982563548"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sigma-hat\n",
    "np.sqrt(res4.scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "aa62e901-50c1-4218-aaaf-42213479db9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.34867221107257745"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# standard deviation of the variability among county-specific Intercepts\n",
    "county_var_int = res4.cov_re.loc[\"county\", \"county\"]\n",
    "np.sqrt(county_var_int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "31fdceee-ee5b-4728-a7ef-858dcaf33a7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3435539748320272"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# standard deviation of the variability among county-specific slopes\n",
    "county_var_slopes = res4.cov_re.loc[\"floor[T.ground]\", \"floor[T.ground]\"]\n",
    "np.sqrt(county_var_slopes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b98791b8-da04-47c6-88e4-57a395e57f5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.3372303607779022"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# correlation between Intercept and slope group-level coefficients\n",
    "county_floor_cov = res4.cov_re.loc[\"county\", \"floor[T.ground]\"]\n",
    "county_floor_cov / (np.sqrt(county_var_int)*np.sqrt(county_var_slopes))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09c13205-8a0d-4223-8963-2495bf49724a",
   "metadata": {},
   "source": [
    "## Discussion\n",
    "\n",
    "### Alternative notations for hierarchical models\n",
    "\n",
    "- IMPORT FROM Gelman & Hill Section 12.5 printout\n",
    "- watch the subscripts!\n",
    "\n",
    "### Computational challenges associated with hierarchical models\n",
    "\n",
    "- centred vs. noncentred representations\n",
    "\n",
    "### Benefits of multilevel models\n",
    "\n",
    "- TODO LIST\n",
    "- Better than repeated measures ANOVA because:\n",
    "  - tells you the direction and magnitude of effect\n",
    "  - can handle more multiple grouping scenarios (e.g. by-item, and by-student)\n",
    "  - works for categorical predictors\n",
    "\n",
    "### Applications\n",
    "\n",
    "- Need for hierarchical models often occurs in social sciences (better than ANOVA)\n",
    "- Hierarchical models are often used for Bayesian meta-analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d0fe5ae-881a-4264-a65b-43870de6ac74",
   "metadata": {},
   "source": [
    "## Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c4f6452-8456-4020-ab45-5365f4ffed0f",
   "metadata": {},
   "source": [
    "### Exercise: mod1u\n",
    "\n",
    "Same model as Example 3 but also include the predictor `log_uranium`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "8bb1576d-4f8d-4f30-b8a0-07b8687204c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       Formula: log_radon ~ 1 + floor + (1|county) + log_uranium\n",
       "        Family: gaussian\n",
       "          Link: mu = identity\n",
       "  Observations: 919\n",
       "        Priors: \n",
       "    target = mu\n",
       "        Common-level effects\n",
       "            Intercept ~ Normal(mu: 1.2246, sigma: 2.1322)\n",
       "            floor ~ Normal(mu: 0.0, sigma: 10.0)\n",
       "            log_uranium ~ Normal(mu: 0.0, sigma: 10.0)\n",
       "        \n",
       "        Group-level effects\n",
       "            1|county ~ Normal(mu: 0.0, sigma: Exponential(lam: 1.0))\n",
       "        \n",
       "        Auxiliary parameters\n",
       "            sigma ~ Exponential(lam: 1.0)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import bambi as bmb\n",
    "\n",
    "covariate_priors = {\n",
    "    \"floor\": bmb.Prior(\"Normal\", mu=0, sigma=10),\n",
    "    \"log_uranium\": bmb.Prior(\"Normal\", mu=0, sigma=10),\n",
    "    \"1|county\": bmb.Prior(\"Normal\", mu=0, sigma=bmb.Prior(\"Exponential\", lam=1)),\n",
    "    \"sigma\": bmb.Prior(\"Exponential\", lam=1),\n",
    "}\n",
    "\n",
    "mod1u = bmb.Model(formula=\"log_radon ~ 1 + floor + (1|county) + log_uranium\",\n",
    "                  priors=covariate_priors,\n",
    "                  data=radon)\n",
    "\n",
    "mod1u"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d9a25ed-3ec8-409b-8138-727922dd9b60",
   "metadata": {},
   "source": [
    "### Exercise: pigs dataset\n",
    "\n",
    "https://bambinos.github.io/bambi/notebooks/multi-level_regression.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "985ccab8-058e-45ce-8ea8-12506d56958b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pig</th>\n",
       "      <th>Evit</th>\n",
       "      <th>Cu</th>\n",
       "      <th>Litter</th>\n",
       "      <th>Start</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Feed</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4601</td>\n",
       "      <td>Evit000</td>\n",
       "      <td>Cu000</td>\n",
       "      <td>1</td>\n",
       "      <td>26.5</td>\n",
       "      <td>26.50000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4601</td>\n",
       "      <td>Evit000</td>\n",
       "      <td>Cu000</td>\n",
       "      <td>1</td>\n",
       "      <td>26.5</td>\n",
       "      <td>27.59999</td>\n",
       "      <td>5.200005</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4601</td>\n",
       "      <td>Evit000</td>\n",
       "      <td>Cu000</td>\n",
       "      <td>1</td>\n",
       "      <td>26.5</td>\n",
       "      <td>36.50000</td>\n",
       "      <td>17.600000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4601</td>\n",
       "      <td>Evit000</td>\n",
       "      <td>Cu000</td>\n",
       "      <td>1</td>\n",
       "      <td>26.5</td>\n",
       "      <td>40.29999</td>\n",
       "      <td>28.500000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4601</td>\n",
       "      <td>Evit000</td>\n",
       "      <td>Cu000</td>\n",
       "      <td>1</td>\n",
       "      <td>26.5</td>\n",
       "      <td>49.09998</td>\n",
       "      <td>45.200001</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>8442</td>\n",
       "      <td>Evit000</td>\n",
       "      <td>Cu175</td>\n",
       "      <td>24</td>\n",
       "      <td>25.7</td>\n",
       "      <td>73.19995</td>\n",
       "      <td>83.800003</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>857</th>\n",
       "      <td>8442</td>\n",
       "      <td>Evit000</td>\n",
       "      <td>Cu175</td>\n",
       "      <td>24</td>\n",
       "      <td>25.7</td>\n",
       "      <td>81.69995</td>\n",
       "      <td>99.800003</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858</th>\n",
       "      <td>8442</td>\n",
       "      <td>Evit000</td>\n",
       "      <td>Cu175</td>\n",
       "      <td>24</td>\n",
       "      <td>25.7</td>\n",
       "      <td>90.29999</td>\n",
       "      <td>115.200001</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>8442</td>\n",
       "      <td>Evit000</td>\n",
       "      <td>Cu175</td>\n",
       "      <td>24</td>\n",
       "      <td>25.7</td>\n",
       "      <td>96.00000</td>\n",
       "      <td>133.200001</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>8442</td>\n",
       "      <td>Evit000</td>\n",
       "      <td>Cu175</td>\n",
       "      <td>24</td>\n",
       "      <td>25.7</td>\n",
       "      <td>103.50000</td>\n",
       "      <td>151.400002</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>861 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Pig     Evit     Cu  Litter  Start     Weight        Feed  Time\n",
       "0    4601  Evit000  Cu000       1   26.5   26.50000         NaN     1\n",
       "1    4601  Evit000  Cu000       1   26.5   27.59999    5.200005     2\n",
       "2    4601  Evit000  Cu000       1   26.5   36.50000   17.600000     3\n",
       "3    4601  Evit000  Cu000       1   26.5   40.29999   28.500000     4\n",
       "4    4601  Evit000  Cu000       1   26.5   49.09998   45.200001     5\n",
       "..    ...      ...    ...     ...    ...        ...         ...   ...\n",
       "856  8442  Evit000  Cu175      24   25.7   73.19995   83.800003     8\n",
       "857  8442  Evit000  Cu175      24   25.7   81.69995   99.800003     9\n",
       "858  8442  Evit000  Cu175      24   25.7   90.29999  115.200001    10\n",
       "859  8442  Evit000  Cu175      24   25.7   96.00000  133.200001    11\n",
       "860  8442  Evit000  Cu175      24   25.7  103.50000  151.400002    12\n",
       "\n",
       "[861 rows x 8 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "dietox = sm.datasets.get_rdataset(\"dietox\", \"geepack\").data\n",
    "dietox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "7ca3acf4-8054-4c94-8ade-b86c889b7537",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [sigma, Intercept, Time, 1|Pig_sigma, 1|Pig_offset, Time|Pig_sigma, Time|Pig_offset]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "19ec62ec67fe458b8f38006fb9aed929",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 8 seconds.\n"
     ]
    }
   ],
   "source": [
    "pigsmodel = bmb.Model(\"Weight ~ Time + (Time|Pig)\", dietox)\n",
    "pigsidata = pigsmodel.fit(random_seed=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "293157e2-5ad9-4e7f-9a0e-fdbeccfe6c92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>15.734</td>\n",
       "      <td>0.566</td>\n",
       "      <td>14.681</td>\n",
       "      <td>16.783</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.012</td>\n",
       "      <td>747.0</td>\n",
       "      <td>1247.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <td>6.933</td>\n",
       "      <td>0.084</td>\n",
       "      <td>6.772</td>\n",
       "      <td>7.084</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>576.0</td>\n",
       "      <td>825.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|Pig_sigma</th>\n",
       "      <td>4.543</td>\n",
       "      <td>0.436</td>\n",
       "      <td>3.816</td>\n",
       "      <td>5.470</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.008</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>2035.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time|Pig_sigma</th>\n",
       "      <td>0.664</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.776</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1162.0</td>\n",
       "      <td>2074.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>2.460</td>\n",
       "      <td>0.065</td>\n",
       "      <td>2.342</td>\n",
       "      <td>2.588</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>5051.0</td>\n",
       "      <td>2703.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "Intercept       15.734  0.566  14.681   16.783      0.021    0.012     747.0   \n",
       "Time             6.933  0.084   6.772    7.084      0.003    0.002     576.0   \n",
       "1|Pig_sigma      4.543  0.436   3.816    5.470      0.012    0.008    1280.0   \n",
       "Time|Pig_sigma   0.664  0.062   0.550    0.776      0.002    0.001    1162.0   \n",
       "sigma            2.460  0.065   2.342    2.588      0.001    0.001    5051.0   \n",
       "\n",
       "                ess_tail  r_hat  \n",
       "Intercept         1247.0   1.00  \n",
       "Time               825.0   1.01  \n",
       "1|Pig_sigma       2035.0   1.00  \n",
       "Time|Pig_sigma    2074.0   1.00  \n",
       "sigma             2703.0   1.00  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(pigsidata, var_names=[\"Intercept\", \"Time\", \"1|Pig_sigma\", \"Time|Pig_sigma\", \"sigma\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "290c9d13-d91b-454d-8c05-d37a270e271a",
   "metadata": {},
   "source": [
    "### Exercise: educational data\n",
    "\n",
    "cf. https://mc-stan.org/users/documentation/case-studies/tutorial_rstanarm.html\n",
    "\n",
    "1.1 Data example\n",
    "We will be analyzing the Gcsemv dataset (Rasbash et al. 2000) from the mlmRev package in R. \n",
    "The data include the General Certificate of Secondary Education (GCSE) exam scores of 1,905 students from 73 schools in England on a science subject. The Gcsemv dataset consists of the following 5 variables:\n",
    "\n",
    "- school: school identifier\n",
    "- student: student identifier\n",
    "- gender: gender of a student (M: Male, F: Female)\n",
    "- written: total score on written paper\n",
    "- course: total score on coursework paper\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ff2cbb1e-99d0-4ba8-913a-b9ac655c84ea",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school</th>\n",
       "      <th>student</th>\n",
       "      <th>gender</th>\n",
       "      <th>written</th>\n",
       "      <th>course</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20920</td>\n",
       "      <td>27</td>\n",
       "      <td>F</td>\n",
       "      <td>39.0</td>\n",
       "      <td>76.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20920</td>\n",
       "      <td>31</td>\n",
       "      <td>F</td>\n",
       "      <td>36.0</td>\n",
       "      <td>87.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20920</td>\n",
       "      <td>42</td>\n",
       "      <td>M</td>\n",
       "      <td>16.0</td>\n",
       "      <td>44.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20920</td>\n",
       "      <td>101</td>\n",
       "      <td>F</td>\n",
       "      <td>49.0</td>\n",
       "      <td>89.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20920</td>\n",
       "      <td>113</td>\n",
       "      <td>M</td>\n",
       "      <td>25.0</td>\n",
       "      <td>17.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   school  student gender  written  course\n",
       "0   20920       27      F     39.0    76.8\n",
       "1   20920       31      F     36.0    87.9\n",
       "2   20920       42      M     16.0    44.4\n",
       "3   20920      101      F     49.0    89.8\n",
       "4   20920      113      M     25.0    17.5"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import pyreadr\n",
    "# path = \"/Users/ivan/Desktop/stats stuff from desktop/Current Bayesian/Hierarchical models/mlmRev\"\n",
    "# Gcsemv_r = pyreadr.read_r(path + '/data/Gcsemv.rda')\n",
    "# Gcsemv_r[\"Gcsemv\"].dropna().to_csv(\"datasets/exercises/gcsemv.csv\", index=False)\n",
    "\n",
    "gcsemv = pd.read_csv(\"datasets/exercises/gcsemv.csv\")\n",
    "gcsemv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "99f9cee9-2e9c-41a5-bab7-33cf186a4134",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       Formula: course ~ 1 + (1 | school)\n",
       "        Family: gaussian\n",
       "          Link: mu = identity\n",
       "  Observations: 1523\n",
       "        Priors: \n",
       "    target = mu\n",
       "        Common-level effects\n",
       "            Intercept ~ Normal(mu: 73.3814, sigma: 41.0781)\n",
       "        \n",
       "        Group-level effects\n",
       "            1|school ~ Normal(mu: 0.0, sigma: HalfNormal(sigma: 41.0781))\n",
       "        \n",
       "        Auxiliary parameters\n",
       "            sigma ~ HalfStudentT(nu: 4.0, sigma: 16.4312)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import bambi as bmb\n",
    "m1 = bmb.Model(formula=\"course ~ 1 + (1 | school)\", data=gcsemv)\n",
    "m1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "a2c15943-c60b-41ab-9ba1-72287f5e5f49",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [sigma, Intercept, 1|school_sigma, 1|school_offset]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b341cb5673d94e199bcb165e3945b804",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 5 seconds.\n"
     ]
    }
   ],
   "source": [
    "idata_m1 = m1.fit(random_seed=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "aa8c1fb7-b7af-4123-9ff5-eb604c713c3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>13.976</td>\n",
       "      <td>0.256</td>\n",
       "      <td>13.522</td>\n",
       "      <td>14.481</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.004</td>\n",
       "      <td>5377.0</td>\n",
       "      <td>2626.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>73.821</td>\n",
       "      <td>1.153</td>\n",
       "      <td>71.596</td>\n",
       "      <td>75.885</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.020</td>\n",
       "      <td>1029.0</td>\n",
       "      <td>1604.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|school_sigma</th>\n",
       "      <td>8.806</td>\n",
       "      <td>0.896</td>\n",
       "      <td>7.165</td>\n",
       "      <td>10.475</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.014</td>\n",
       "      <td>1159.0</td>\n",
       "      <td>1942.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|school[20920]</th>\n",
       "      <td>-6.962</td>\n",
       "      <td>5.171</td>\n",
       "      <td>-17.310</td>\n",
       "      <td>1.961</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.100</td>\n",
       "      <td>6012.0</td>\n",
       "      <td>2847.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|school[22520]</th>\n",
       "      <td>-15.657</td>\n",
       "      <td>2.117</td>\n",
       "      <td>-19.509</td>\n",
       "      <td>-11.573</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.030</td>\n",
       "      <td>2264.0</td>\n",
       "      <td>2862.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|school[76531]</th>\n",
       "      <td>-11.166</td>\n",
       "      <td>5.149</td>\n",
       "      <td>-20.527</td>\n",
       "      <td>-1.389</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.083</td>\n",
       "      <td>6334.0</td>\n",
       "      <td>2593.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|school[76631]</th>\n",
       "      <td>2.101</td>\n",
       "      <td>2.818</td>\n",
       "      <td>-3.220</td>\n",
       "      <td>7.340</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.046</td>\n",
       "      <td>3565.0</td>\n",
       "      <td>2924.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|school[77207]</th>\n",
       "      <td>-3.242</td>\n",
       "      <td>3.275</td>\n",
       "      <td>-9.212</td>\n",
       "      <td>2.887</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.049</td>\n",
       "      <td>4210.0</td>\n",
       "      <td>3080.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|school[84707]</th>\n",
       "      <td>4.050</td>\n",
       "      <td>7.479</td>\n",
       "      <td>-10.230</td>\n",
       "      <td>17.833</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.129</td>\n",
       "      <td>5880.0</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|school[84772]</th>\n",
       "      <td>8.568</td>\n",
       "      <td>3.587</td>\n",
       "      <td>1.524</td>\n",
       "      <td>15.023</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.058</td>\n",
       "      <td>4346.0</td>\n",
       "      <td>2855.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "sigma            13.976  0.256  13.522   14.481      0.003    0.004    5377.0   \n",
       "Intercept        73.821  1.153  71.596   75.885      0.036    0.020    1029.0   \n",
       "1|school_sigma    8.806  0.896   7.165   10.475      0.026    0.014    1159.0   \n",
       "1|school[20920]  -6.962  5.171 -17.310    1.961      0.067    0.100    6012.0   \n",
       "1|school[22520] -15.657  2.117 -19.509  -11.573      0.045    0.030    2264.0   \n",
       "...                 ...    ...     ...      ...        ...      ...       ...   \n",
       "1|school[76531] -11.166  5.149 -20.527   -1.389      0.065    0.083    6334.0   \n",
       "1|school[76631]   2.101  2.818  -3.220    7.340      0.047    0.046    3565.0   \n",
       "1|school[77207]  -3.242  3.275  -9.212    2.887      0.051    0.049    4210.0   \n",
       "1|school[84707]   4.050  7.479 -10.230   17.833      0.099    0.129    5880.0   \n",
       "1|school[84772]   8.568  3.587   1.524   15.023      0.054    0.058    4346.0   \n",
       "\n",
       "                 ess_tail  r_hat  \n",
       "sigma              2626.0    1.0  \n",
       "Intercept          1604.0    1.0  \n",
       "1|school_sigma     1942.0    1.0  \n",
       "1|school[20920]    2847.0    1.0  \n",
       "1|school[22520]    2862.0    1.0  \n",
       "...                   ...    ...  \n",
       "1|school[76531]    2593.0    1.0  \n",
       "1|school[76631]    2924.0    1.0  \n",
       "1|school[77207]    3080.0    1.0  \n",
       "1|school[84707]    3050.0    1.0  \n",
       "1|school[84772]    2855.0    1.0  \n",
       "\n",
       "[76 rows x 9 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(idata_m1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "b4a04125-5007-4fb4-b900-70b7d5708e37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       Formula: course ~ gender + (1 + gender|school)\n",
       "        Family: gaussian\n",
       "          Link: mu = identity\n",
       "  Observations: 1523\n",
       "        Priors: \n",
       "    target = mu\n",
       "        Common-level effects\n",
       "            Intercept ~ Normal(mu: 73.3814, sigma: 53.4663)\n",
       "            gender ~ Normal(mu: 0.0, sigma: 83.5292)\n",
       "        \n",
       "        Group-level effects\n",
       "            1|school ~ Normal(mu: 0.0, sigma: HalfNormal(sigma: 53.4663))\n",
       "            gender|school ~ Normal(mu: 0.0, sigma: HalfNormal(sigma: 83.5292))\n",
       "        \n",
       "        Auxiliary parameters\n",
       "            sigma ~ HalfStudentT(nu: 4.0, sigma: 16.4312)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3 = bmb.Model(formula=\"course ~ gender + (1 + gender|school)\", data=gcsemv)\n",
    "m3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "0107426b-b5cb-4f4f-b046-37dca8054f09",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [sigma, Intercept, gender, 1|school_sigma, 1|school_offset, gender|school_sigma, gender|school_offset]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ac68b133ce6241588bfa539ceb7b9c2a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 5 seconds.\n"
     ]
    }
   ],
   "source": [
    "idata_m3 = m3.fit(random_seed=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15a926f9-a5c9-46ba-8b2b-27e9bed78aba",
   "metadata": {},
   "source": [
    "### Exercise: sleepstudy dataset\n",
    "\n",
    "- Description: Contains reaction times of subjects under sleep deprivation conditions.\n",
    "- Source: Featured in the R package lme4.\n",
    "- Application: Demonstrates linear mixed-effects modeling with random slopes and intercepts.\n",
    "\n",
    "\n",
    "Links:\n",
    "- https://bambinos.github.io/bambi/notebooks/sleepstudy.html\n",
    "- https://www.tjmahr.com/plotting-partial-pooling-in-mixed-effects-models/#the-sleepstudy-dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "60c65c68-1f32-4814-9969-e90f9ee0a30d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Reaction</th>\n",
       "      <th>Days</th>\n",
       "      <th>Subject</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>249.5600</td>\n",
       "      <td>0</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>258.7047</td>\n",
       "      <td>1</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>250.8006</td>\n",
       "      <td>2</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>321.4398</td>\n",
       "      <td>3</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>356.8519</td>\n",
       "      <td>4</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>329.6076</td>\n",
       "      <td>5</td>\n",
       "      <td>372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>334.4818</td>\n",
       "      <td>6</td>\n",
       "      <td>372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>343.2199</td>\n",
       "      <td>7</td>\n",
       "      <td>372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>369.1417</td>\n",
       "      <td>8</td>\n",
       "      <td>372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>364.1236</td>\n",
       "      <td>9</td>\n",
       "      <td>372</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Reaction  Days  Subject\n",
       "0    249.5600     0      308\n",
       "1    258.7047     1      308\n",
       "2    250.8006     2      308\n",
       "3    321.4398     3      308\n",
       "4    356.8519     4      308\n",
       "..        ...   ...      ...\n",
       "175  329.6076     5      372\n",
       "176  334.4818     6      372\n",
       "177  343.2199     7      372\n",
       "178  369.1417     8      372\n",
       "179  364.1236     9      372\n",
       "\n",
       "[180 rows x 3 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sleepstudy = bmb.load_data(\"sleepstudy\")\n",
    "sleepstudy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "622c7907-9545-42d9-9ce8-e41595285fb1",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "### Exercise: tadpoles (BONUS)\n",
    "\n",
    "https://www.youtube.com/watch?v=iwVqiiXYeC4\n",
    "\n",
    "logistic regression model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d5a591d-d134-4ad5-bb57-7eeffcb4ed25",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Links"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d66d8f2a-116e-4c92-8bf0-fc5a0d492364",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "33a9c3f6-ff51-4dbf-94a3-115da563c33a",
   "metadata": {},
   "source": [
    "## EXTRA MATERIAL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d09187c-60e2-4c00-b52a-b87d6e4f7c18",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d270281-24b9-4ffc-903f-fffb065f29b1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "caa6a877-a63d-466f-827c-e807d3e6d6d5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9dd9d7ae-34d6-4d77-b908-08af1e0c17b2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5f80877-6f52-4828-aa35-54c55def860d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}