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    "# 23. Introduction to Gaussian processes\n",
    "\n",
    "[Data set download](https://s3.amazonaws.com/bebi103.caltech.edu/data/wolfenden_arrhenius.csv)\n",
    "\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "nbsphinx": "hidden",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Colab setup ------------------\n",
    "import os, shutil, sys, subprocess, urllib.request\n",
    "if \"google.colab\" in sys.modules:\n",
    "    cmd = \"pip install --upgrade iqplot colorcet bebi103 arviz cmdstanpy watermark\"\n",
    "    process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n",
    "    stdout, stderr = process.communicate()\n",
    "    from cmdstanpy.install_cmdstan import latest_version\n",
    "    cmdstan_version = latest_version()\n",
    "    cmdstan_url = f\"https://github.com/stan-dev/cmdstan/releases/download/v{cmdstan_version}/\"\n",
    "    fname = f\"colab-cmdstan-{cmdstan_version}.tgz\"\n",
    "    urllib.request.urlretrieve(cmdstan_url + fname, fname)\n",
    "    shutil.unpack_archive(fname)\n",
    "    os.environ[\"CMDSTAN\"] = f\"./cmdstan-{cmdstan_version}\"\n",
    "    data_path = \"https://s3.amazonaws.com/bebi103.caltech.edu/data/\"\n",
    "else:\n",
    "    data_path = \"../data/\"\n",
    "# ------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import cmdstanpy\n",
    "import arviz as az\n",
    "\n",
    "import bebi103\n",
    "\n",
    "import bokeh.io\n",
    "bokeh.io.output_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "## Predicting using posterior estimates\n",
    "\n",
    "The following happens a lot in science. We vary variables $\\mathbf{x}$ (like time, pH, etc.) and make observations $\\mathbf{y}$. We perform a regression using some theoretical function $f(\\mathbf{x})$, which describes how we expect $y$ to vary with $\\mathbf{x}$. We then can have a pretty good idea what we would measure for some other value of $\\mathbf{x}$. By making a few measurements, the regression helps us say things more generally, even for $\\mathbf{x}$ values we didn't explicitly use in an experiment.\n",
    "\n",
    "We have seen this in this class. We perform a regression, getting samples from the posterior distribution. We then sample out of the posterior predictive distribution to get what we might expect for performing an experiment, perhaps with a difference $x$ values. In practice, this is performing a posterior predictive check with some values of $x$ that we hadn't used in a measurement.\n",
    "\n",
    "This is an example of **parametric inference**, in which we have a specific mathematical model in mind, complete with parameters. But what if we did not have a specific function in mind? Rather, we just would like to be able to *predict* what value of $y$ we might get for some untested $x$ value and we do not really care what the underlying model is. In other words, we just would like to consider a family of functions that do not vary too rapidly or with too large of amplitude. There are an infinity of such functions. This is an example of **nonparametric inference**. In a Bayesian context, nonparametric inference involves consideration of an infinite number of models. We will explore nonparametric inference in the context of **Gaussian processes**.\n",
    "\n",
    "As we go about this, it will be useful to have a concrete example in mind."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An example data set\n",
    "\n",
    "For concreteness, let's consider a specific example, the breakdown of $\\alpha$-1-methylglucopyranoside, a key step in the hydrolysis of cellulose. This is featured in a [nice paper by Wolfenden and Snider](https://dx.doi.org/10.1021/ar000058i) about the power of enzymes as catalysts. This example shows that glucoside hydrolysis is incredibly slow in the absence of enzymes. Let's take a quick look at measured rate constants $k$ as a function of temperature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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   "source": [
    "df = pd.read_csv(os.path.join(data_path, 'wolfenden_arrhenius.csv'))\n",
    "df['T (K)'] = 1000 / df['1000/T (1/K)']\n",
    "df['k (1/s)'] = np.exp(df['ln k (1/s)'])\n",
    "\n",
    "p = bokeh.plotting.figure(\n",
    "    frame_height=250,\n",
    "    frame_width=350,\n",
    "    x_axis_label='T (K)',\n",
    "    y_axis_label='k (1/s)'\n",
    ")\n",
    "p.circle(source=df, x='T (K)', y='k (1/s)')\n",
    "\n",
    "bokeh.io.show(p)"
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  {
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   "source": [
    "The chemical rate constant is often well described by the **Arrhenius relation**,\n",
    "\n",
    "\\begin{align}\n",
    "k = A \\mathrm{e}^{-E_a/k_BT},\n",
    "\\end{align}\n",
    "\n",
    "where $E_a$ is the activation energy that catalysts serve to decrease. For ease of notation, I will define units such that $k_B = 1$, and will convert back to familiar units when needed. So, we can write the Arrhenius rate law as\n",
    "\n",
    "\\begin{align}\n",
    "k = A \\mathrm{e}^{-E_a/T}.\n",
    "\\end{align}\n",
    "\n",
    "In an experiment, we can vary (and exactly measure) $T$, so there are two parameters, $A$ and $E_a$. A generative model for this might be (where the units of $A$ are in seconds and we again abuse a logarithm to make specification of the prior easier)\n",
    "\n",
    "\\begin{align}\n",
    "&\\log_{10}E_a \\sim \\text{Normal}(-6, 6),\\\\[1em]\n",
    "&E_a = 10^{\\log_{10} E_a},\\\\[1em]\n",
    "&\\log_{10} A \\sim \\text{Normal}(-6, 6), \\\\[1em]\n",
    "&A = 10^{\\log_{10} A}, \\\\[1em]\n",
    "&\\sigma \\sim \\text{HalfNorm}(0.01), \\\\[1em]\n",
    "&\\mu_i = A \\mathrm{e}^{-E_a/T_i}\\;\\forall i,\\\\[1em]\n",
    "&k_i \\sim \\text{Norm}(\\mu_i, \\sigma)\\;\\forall i.\n",
    "\\end{align}\n",
    "\n",
    "Just for fun, we can perform parameter estimation for this model. The Stan code is\n",
    "\n",
    "```stan\n",
    "data {\n",
    "  int<lower=1> N;\n",
    "  array[N] real T;\n",
    "  array[N] real k;\n",
    "\n",
    "  int<lower=1> N_ppc;\n",
    "  array[N_ppc] real T_ppc;\n",
    "}\n",
    "\n",
    "\n",
    "parameters {\n",
    "  real log10_Ea;\n",
    "  real log10_A;\n",
    "  real<lower=0> sigma;\n",
    "}\n",
    "\n",
    "\n",
    "transformed parameters {\n",
    "  real Ea = 10^log10_Ea;\n",
    "  real A = 10^log10_A;\n",
    "\n",
    "  array[N] real mu;\n",
    "  for (i in 1:N) {\n",
    "    mu[i] = A * exp(-Ea / T[i]);\n",
    "  } \n",
    "}\n",
    "\n",
    "\n",
    "model {\n",
    "  sigma ~ normal(0.0, 0.01);\n",
    "  log10_Ea ~ normal(1.0, 6.0);\n",
    "  log10_A ~ normal(1.0, 6.0);\n",
    "\n",
    "  k ~ normal(mu, sigma);\n",
    "}\n",
    "\n",
    "\n",
    "generated quantities {\n",
    "  array[N_ppc] real k_ppc;\n",
    "  for (i in 1:N_ppc) {\n",
    "    k_ppc[i] = normal_rng(A * exp(-Ea / T_ppc[i]), sigma);\n",
    "  }\n",
    "}\n",
    "```\n",
    "\n",
    "Note that we have chosen a prior on the activation energy to allow for very large values, which is what we might expect for uncatalyzed reactions. Let's perform the inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5dd69d0cf85a4fa38b168819643fc856",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "chain 1 |          | 00:00 Status"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5b98f15a867b41b886e259f95a5e3958",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "chain 2 |          | 00:00 Status"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "249a8dcbc5f64c58bce35e6a93e72d3c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "chain 3 |          | 00:00 Status"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b5187da66e334548bcadbf15d4d14e29",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "chain 4 |          | 00:00 Status"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                                                                                                                                                                                                                                                \n"
     ]
    }
   ],
   "source": [
    "# Generate data dictionary\n",
    "T = df[\"T (K)\"].values\n",
    "k = df[\"k (1/s)\"].values\n",
    "N = len(T)\n",
    "T_ppc = np.linspace(450, 530, 200)\n",
    "N_ppc = len(T_ppc)\n",
    "\n",
    "data = dict(N=N, N_ppc=N_ppc, T=T, T_ppc=T_ppc, k=k)\n",
    "\n",
    "# Compile and sample\n",
    "with bebi103.stan.disable_logging():\n",
    "    sm_parametric = cmdstanpy.CmdStanModel(stan_file=\"parametric.stan\")\n",
    "\n",
    "    samples = sm_parametric.sample(\n",
    "        data=data,\n",
    "        adapt_delta=0.99,\n",
    "        max_treedepth=15,\n",
    "        iter_warmup=4000,\n",
    "        iter_sampling=4000,\n",
    "    )\n",
    "    \n",
    "samples = az.from_cmdstanpy(posterior=samples, posterior_predictive=\"k_ppc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As usual, we should check the diagnostics. (I actually did this with the default sampling settings, and found that I needed to take more samples and increase `adapt_delta` and the maximum tree depth.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Effective sample size looks reasonable for all parameters.\n",
      "\n",
      "Rhat looks reasonable for all parameters.\n",
      "\n",
      "0 of 16000 (0.0%) iterations ended with a divergence.\n",
      "\n",
      "0 of 16000 (0.0%) iterations saturated the maximum tree depth of 15.\n",
      "\n",
      "E-BFMI indicated no pathological behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bebi103.stan.check_all_diagnostics(samples, max_treedepth=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're ok on the diagnostics. Let's take a look at posterior predictive checks to see how the model performed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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\"},\"shape\":[200],\"dtype\":\"float64\",\"order\":\"little\"}]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1101\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1102\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1097\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":\"#084594\",\"line_width\":2}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1098\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":\"#084594\",\"line_alpha\":0.1,\"line_width\":2}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1099\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":\"#084594\",\"line_alpha\":0.2,\"line_width\":2}}}},{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1109\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1103\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1104\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1105\"},\"data\":{\"type\":\"map\",\"entries\":[[\"x\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"ud2lfuSrfEBIwjCFHy99QE+R06Bdj31ACsWq5wcufkCA6LWztKh+QFL5bF0Hon9AUvlsXQeif0D1CRCoxwiAQMWLIAG4VoBAKs3HDMh3gEA=\"},\"shape\":[10],\"dtype\":\"float64\",\"order\":\"little\"}],[\"y\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"/o+RalsiWj+jYrhisQdgP8E7TuXf1mI/jATFgMifaj+9umKF5mFyP1mEE5l5gHo/Ovf0Bh5PeT+KtlxcXVyAP1DRf0Ez44E/4uhEBeZ8gz8=\"},\"shape\":[10],\"dtype\":\"float64\",\"order\":\"little\"}]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1110\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1111\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Circle\",\"id\":\"p1106\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"size\":{\"type\":\"value\",\"value\":2},\"line_color\":{\"type\":\"value\",\"value\":\"orange\"},\"fill_color\":{\"type\":\"value\",\"value\":\"orange\"},\"hatch_color\":{\"type\":\"value\",\"value\":\"orange\"}}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Circle\",\"id\":\"p1107\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"size\":{\"type\":\"value\",\"value\":2},\"line_color\":{\"type\":\"value\",\"value\":\"orange\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.1},\"fill_color\":{\"type\":\"value\",\"value\":\"orange\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.1},\"hatch_color\":{\"type\":\"value\",\"value\":\"orange\"},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.1}}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Circle\",\"id\":\"p1108\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"size\":{\"type\":\"value\",\"value\":2},\"line_color\":{\"type\":\"value\",\"value\":\"orange\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.2},\"fill_color\":{\"type\":\"value\",\"value\":\"orange\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.2},\"hatch_color\":{\"type\":\"value\",\"value\":\"orange\"},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.2}}}}}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p1055\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p1069\"},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p1070\",\"attributes\":{\"renderers\":\"auto\"}},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p1071\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p1072\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"top_units\":\"canvas\",\"bottom_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"SaveTool\",\"id\":\"p1073\"},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p1074\"},{\"type\":\"object\",\"name\":\"HelpTool\",\"id\":\"p1075\"}]}},\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1064\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1065\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1066\"},\"axis_label\":\"k (1/sec)\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1067\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1059\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1060\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1061\"},\"axis_label\":\"T (K)\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1062\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1063\",\"attributes\":{\"axis\":{\"id\":\"p1059\"}}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1068\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1064\"}}}],\"frame_width\":400,\"frame_height\":325}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"toggle_value1\",\"properties\":[{\"name\":\"active_icons\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"options\",\"kind\":\"Any\",\"default\":{\"type\":\"map\",\"entries\":[[\"favorite\",\"heart\"]]}},{\"name\":\"value\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"_reactions\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"_base_url\",\"kind\":\"Any\",\"default\":\"https://tabler-icons.io/static/tabler-icons/icons/\"}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"value\",\"kind\":\"Any\",\"default\":null},{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
       "  const render_items = [{\"docid\":\"388986c0-e6dd-4875-bc9c-fd713f9f654f\",\"roots\":{\"p1047\":\"efeca8d7-f22d-4031-a45b-a58aa88c7f34\"},\"root_ids\":[\"p1047\"]}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1047"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "k_ppc = samples.posterior_predictive['k_ppc'].stack(\n",
    "    {\"sample\": (\"chain\", \"draw\")}\n",
    ").transpose(\"sample\", \"k_ppc_dim_0\")\n",
    "\n",
    "bokeh.io.show(\n",
    "    bebi103.viz.predictive_regression(\n",
    "        k_ppc,\n",
    "        samples_x=T_ppc,\n",
    "        data=np.vstack((T, k)).transpose(),\n",
    "        x_axis_label='T (K)',\n",
    "        y_axis_label='k (1/sec)',\n",
    "        x_range=[T_ppc.min(), T_ppc.max()],\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These look good; the model can generate the data set. Now, let's look at the parameter values. It is better to look at $A$ on a logarithmic scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\"},\"shape\":[10000],\"dtype\":\"float64\",\"order\":\"little\"}],[\"sigma\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   \"},\"shape\":[10000],\"dtype\":\"int32\",\"order\":\"little\"}],[\"draw__\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               \"},\"shape\":[10000],\"dtype\":\"bool\",\"order\":\"little\"}]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1208\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1209\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Circle\",\"id\":\"p1204\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"Ea\"},\"y\":{\"type\":\"field\",\"field\":\"log10_A\"},\"size\":{\"type\":\"value\",\"value\":2},\"line_alpha\":{\"type\":\"value\",\"value\":0.02},\"fill_color\":{\"type\":\"value\",\"value\":\"black\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.02},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.02}}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Circle\",\"id\":\"p1205\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"Ea\"},\"y\":{\"type\":\"field\",\"field\":\"log10_A\"},\"size\":{\"type\":\"value\",\"value\":2},\"line_alpha\":{\"type\":\"value\",\"value\":0},\"fill_color\":{\"type\":\"value\",\"value\":\"black\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.1}}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Circle\",\"id\":\"p1206\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"Ea\"},\"y\":{\"type\":\"field\",\"field\":\"log10_A\"},\"size\":{\"type\":\"value\",\"value\":2},\"line_alpha\":{\"type\":\"value\",\"value\":0.2},\"fill_color\":{\"type\":\"value\",\"value\":\"black\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.2},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.2}}}}},{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1216\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1123\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1124\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1125\"},\"data\":{\"type\":\"map\",\"entries\":[[\"index\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"\"},\"shape\":[0],\"dtype\":\"int32\",\"order\"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       "  const render_items = [{\"docid\":\"180d73ae-f3f8-4656-9cab-f60f1eb94a3a\",\"roots\":{\"p1417\":\"c25d9700-7441-4413-ba6d-76833a4883db\"},\"root_ids\":[\"p1417\"]}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
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       "        clearInterval(timer);\n",
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       "        attempts++;\n",
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       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
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   "source": [
    "bokeh.io.show(\n",
    "    bebi103.viz.corner(\n",
    "        samples,\n",
    "        parameters=[\"Ea\", \"log10_A\", \"sigma\"],\n",
    "    )\n",
    ")"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see extremely strong correlation between the activation energy and the pre-exponential factor. We can identify both parameters, but they have broad distributions, varying over almost two orders of magnitude in the case of $A$.\n",
    "\n",
    "In doing this analysis, we got something important: physically meaningful parameter values. We also got the ability to *predict* what the rate constant would be for a temperature for which we have no measurement. The model affords us that. But what if only this prediction is important to us, and not the physical model? Or maybe we just cannot come up with a physical model because we don't know enough about chemical reactions kinetics to do so. We would nonetheless like to make predictions about rate constants and different temperatures. This is where **Gaussian processes** can be useful, and we will use this example as a data set to explore Gaussian processes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processes and nonparametric Bayesian inference\n",
    "\n",
    "Remember that Bayes's Theorem applies to any logical conjecture. It even applies to *functions*! So, imagine we have observed data $\\mathbf{X}$ and $\\mathbf{y}$. I use a capital $\\mathbf{X}$ here to allow for multidimensional dependent variables. For example, we might want to study both temperature and pH dependence of a rate constant. In this case, each row of $\\mathbf{X}$ is a pH, temperature pair. We define row $i$ of $\\mathbf{X}$ to be $\\mathbf{x}_i$.\n",
    "\n",
    "We expect that for each observation, $y_i = f(\\mathbf{x}_i) + \\epsilon_i$, where $\\epsilon_i$ is some measurement error and/or inherent stochasticity and $f(x)$ is an *unknown* function of $\\mathbf{x}$. We can still write Bayes's Theorem.\n",
    "\n",
    "\\begin{align}\n",
    "\\pi(f\\mid \\mathbf{y}, \\mathbf{X}) = \\frac{\\pi(\\mathbf{y}, \\mathbf{X} \\mid f)\\,\\pi(f)}{\\pi(\\mathbf{y}, \\mathbf{X})}.\n",
    "\\end{align}\n",
    "\n",
    "To avoid confusion with the symbol $f$ for the unknown functions, we will not use $f$ for the likelihood, nor $g$ for the prior and posterior, instead using $\\pi$ for all probabilities and probability densities.\n",
    "\n",
    "It may seem strange to write a probability of functions, but remember that this is allowed in the Bayesian interpretation of probability. We call a probability distribution over functions a **process**. So, the posterior, $\\pi(f\\mid \\mathbf{y}, \\mathbf{X})$, and the prior, $\\pi(f)$, are processes.\n",
    "\n",
    "Since we are primarily interested in predicting new measurements, we want to compute the posterior predictive distribution. Let $\\mathbf{x}_*$ be a set of $x$-values for which we want predictions of the corresponding $y$-values, $f(\\mathbf{x}_*)$. Then, assuming for a moment that we can compute the posterior, we can write a posterior predictive distribution,\n",
    "\n",
    "\\begin{align}\n",
    "\\pi(f(\\mathbf{x}_*) \\mid \\mathbf{x}_*, \\mathbf{y}, \\mathbf{X}) = \\int \\mathrm{d}f\\,\\pi(f(\\mathbf{x}_*)\\mid \\mathbf{x}_*, f)\\, \\pi(f\\mid \\mathbf{y}, \\mathbf{X}).\n",
    "\\end{align}\n",
    "\n",
    "\n",
    "This looks exactly the same as the posterior predictive distribution for parametric regression. Instead of writing a probability distribution over infinitely many parameter values in the parametric setting, we are now writing a process over infinitely many functions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gaussian processes with a finite number of points\n",
    "\n",
    "How can we treat a probability distribution over functions? We can instead define a probability distribution over the function's *values* at some arbitrary points. So, imagine we get function values at points $\\mathbf{X}$, with $N$ total observations. We can define a **joint distribution**, $\\pi(f(\\mathbf{x}_1), \\ldots, f(\\mathbf{x}_N))$. We use this probability density as a drop-in replacement for a process.\n",
    "\n",
    "What distribution should we choose for this joint distribution? We can choose many distributions for this, but we might choose a joint multivariate Normal (a.k.a. Gaussian) distribution. This defines a **Gaussian process**, or GP. To define the joint distribution, then, we need to define the two parameters of a multivariate Gaussian distribution, its mean and covariance. The mean must be defined for an arbitrary point $\\mathbf{x}$, and the covariance for an arbitrary *pair* of points $\\mathbf{x}, \\mathbf{x}'$. Thus, the **mean function**, $m(\\mathbf{x})$ and the covariance function, usually referred to as a **kernel function**, $k(\\mathbf{x}, \\mathbf{x}')$, uniquely define a Gaussian process. We can write a Gaussian process as\n",
    "\n",
    "\\begin{align}\n",
    "f(\\mathbf{x}) \\mid \\theta_m, \\theta_k \\sim \\text{GP}(m(\\mathbf{x} ; \\theta_m), k(\\mathbf{x}, \\mathbf{x}' ; \\theta_k)),\n",
    "\\end{align}\n",
    "\n",
    "where $\\theta_m$ and $\\theta_k$ are sets of **hyperparameters** that parametrize the mean and kernel functions.\n",
    "\n",
    "Because in practice we compute $f(\\mathbf{x}_*)$ in order to get the a picture of what the nonparametric functions given by a Gaussian process look like (that is, we compute the value of the function at a finite set of points $\\mathbf{x}_*$), we can write Bayes's theorem again as\n",
    "\n",
    "\\begin{align}\n",
    "\\pi(f(\\mathbf{x}_*)\\mid \\mathbf{x}_*, \\mathbf{y}, \\mathbf{X}) = \\frac{\\pi(\\mathbf{y}, \\mathbf{X}\\mid f(\\mathbf{x}_*), \\mathbf{x}_*)\\,\\pi(f(\\mathbf{x}_*) \\mid \\mathbf{x}_*)}{\\pi(\\mathbf{y}, \\mathbf{X})}.\n",
    "\\end{align}\n",
    "\n",
    "Or, if we want to evaluate $f$ at multidimensional points, $\\mathbf{X}_*$,\n",
    "\n",
    "\\begin{align}\n",
    "\\pi(f(\\mathbf{X}_*)\\mid \\mathbf{X}_*, \\mathbf{y}, \\mathbf{X}) = \\frac{\\pi(\\mathbf{y}, \\mathbf{X}\\mid f(\\mathbf{X}_*), \\mathbf{X}_*)\\,\\pi(f(\\mathbf{X}_*) \\mid \\mathbf{X}_*)}{\\pi(\\mathbf{y}, \\mathbf{X})}.\n",
    "\\end{align}\n",
    "\n",
    "The values of the nonparametric function, $f(\\mathbf{X}_*)$ are often referred to as **latent variables** because they are variables that are not directly observed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The mean function and centering and scaling\n",
    "\n",
    "In a purely nonparametric approach, we almost always take the mean function to be zero; $m(\\mathbf{x}) = 0$. We may, however, wish to do a **semi-parametric regression** and introduce an explicit bias via $m(\\mathbf{x})$. We often do this when we have, e.g., count data, that we cannot really transform such that the mean is zero.\n",
    "\n",
    "In general in machine learning applications, and many nonparametric contexts in general, it is good practice to **center and scale** the observed data to improve performance of your algorithms. I will not get into the details of this here (though see [this series of blog posts by Hugo Bowne-Anderson on the topic](https://www.datacamp.com/community/tutorials/preprocessing-in-data-science-part-1-centering-scaling-and-knn), but rather will encourage you to center and scale your data before performing inference with a GP, if you can. Specifically, if $\\bar{\\mathbf{y}}$ is the arithmetic mean of observations $\\mathbf{y}$, and $s_\\mathbf{y}$ is the sample standard deviation of $\\mathbf{y}$, then you should apply a linear transformation of $\\mathbf{y}$ to get a centered and scaled version.\n",
    "\n",
    "\\begin{align}\n",
    "\\mathbf{y}_\\mathrm{scaled} = \\frac{\\mathbf{y} - \\bar{\\mathbf{y}}}{s_\\mathbf{y}}.\n",
    "\\end{align}\n",
    "\n",
    "You should then work with $\\mathbf{y}_\\mathrm{scaled}$. You should do this to all $\\mathbf{y}$ values. You can then apply the inverse linear transformation to get back your original values.\n",
    "\n",
    "\\begin{align}\n",
    "\\mathbf{y} = s_\\mathbf{y} \\mathbf{y}_\\mathrm{scaled} + \\bar{\\mathbf{y}}.\n",
    "\\end{align}\n",
    "\n",
    "Henceforth, we will assume we are working with centered and scaled data such that the mean functions of Gaussian process priors are zero."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The kernel and covariance matrix\n",
    "\n",
    "The covariance function, $k(\\mathbf{x}, \\mathbf{x}')$ is called a **kernel**. It must have certain properties. Remember that it defines the entries of a covariance matrix of a multivariate Normal distribution. Specifically, let's say that $\\mathbf{X}$ has $n$ rows. Then, we can define an $n\\times n$ matrix, $\\mathsf{K}(\\mathbf{X}, \\mathbf{X}')$ that has entries\n",
    "\n",
    "\\begin{align}\n",
    "K_{ij} = k(\\mathbf{x}_i, \\mathbf{x}'_j).\n",
    "\\end{align}\n",
    "\n",
    "This matrix is a covariance matrix, which is a special case of a **Gram matrix**. Because this is a joint Gaussian distribution, the covariance matrix $\\mathsf{K}(\\mathbf{X}, \\mathbf{X}')$ must be positive definite for *any* $\\mathbf{X}$, $\\mathbf{X}'$. This puts a restriction on what kernels that are allowed. Some common kernels that result in positive definite covariance matrices are shown below.\n",
    "\n",
    "\\begin{align}\n",
    "&\\text{linear: } k(\\mathbf{x}, \\mathbf{x}') = \\sigma_b^2 + \\sum_i \\sigma_i^2 x_i\\,x_i' \\\\[1em]\n",
    "&\\text{polynomial: } k(\\mathbf{x}, \\mathbf{x}') =  (\\sigma_b^2 + \\sigma_p^2\\,\\mathbf{x}^\\mathsf{T} \\cdot \\mathbf{x}')^d, \\;\\;d = \\{1, 2, 3, \\ldots\\}\\\\[1em]\n",
    "&\\text{squared exponential (SE): } k(\\mathbf{x}, \\mathbf{x}') = \\alpha^2\\,\\exp\\left[-\\frac{\\left\\Vert\\mathbf{x} - \\mathbf{x}'\\right\\Vert_2^2}{2\\rho^2}\\right] \\\\[1em]\n",
    "&\\text{Matérn: }k(\\mathbf{x}, \\mathbf{x}') = \\alpha^2\\,\\frac{2^{1-\\nu}\\,\\beta^\\nu}{\\Gamma(\\nu)}\\,K_\\nu\\left(\\beta\\right), \\text{ where } \\beta = \\left(\\frac{2\\nu\\left\\Vert\\mathbf{x} - \\mathbf{x}'\\right\\Vert_2^2}{\\rho^2}\\right)^{\\frac{1}{2}}\\\\[1em]\n",
    "&\\text{Matérn ($\\nu=5/2$): } k(\\mathbf{x}, \\mathbf{x}') = \\alpha^2\\left(1 + \\left(\\frac{5\\left\\Vert\\mathbf{x} - \\mathbf{x}'\\right\\Vert_2^2}{\\rho^2}\\right)^{\\frac{1}{2}} + \\frac{5 \\left\\Vert\\mathbf{x} - \\mathbf{x}'\\right\\Vert_2^2}{3\\rho^2}\\right)  \\exp\\left[-\\left(\\frac{5\\left\\Vert\\mathbf{x} - \\mathbf{x}'\\right\\Vert_2^2}{\\rho^2}\\right)^{\\frac{1}{2}}\\right],\\\\[1em]\n",
    "&\\text{periodic: } k(\\mathbf{x}, \\mathbf{x}') = \\alpha^2\\,\\exp\\left[-\\frac{2}{\\rho^2}\\,\\sin^2\\left(\\frac{\\pi}{T}\\,\\sqrt{\\Vert\\mathbf{x} - \\mathbf{x}'\\Vert_2^2}\\right)\\right].\n",
    "\\end{align}\n",
    "\n",
    "where\n",
    "\n",
    "\\begin{align}\n",
    "\\left\\Vert\\mathbf{x} - \\mathbf{x}'\\right\\Vert_2^2 = (\\mathbf{x} - \\mathbf{x}')^\\mathsf{T}\\cdot(\\mathbf{x} - \\mathbf{x}')\n",
    "\\end{align}\n",
    "\n",
    "is the 2-norm, and $K_\\nu$ is a modified Bessel function of the second kind. For the periodic kernel, which is used to model functions that vary periodically, $T$ is the period. I have given the special case of a Matérn kernel with $\\nu = 5/2$, since that is widely used. \n",
    "\n",
    "The squared exponential (SE) kernel is probably the most widely used, and it is also known as the **exponentiated quadratic kernel** or the **radial basis function kernel**, and it is built in to Stan (where it is referred to as the exponentiated quadratic kernel). In the spirit of principled modeling, in which we go from simple models to more complex models, it is worth noting that the SE kernel is the $\\nu \\to \\infty$ limit of the Matérn kernel. For finite $\\nu$, functions realized from the Matérn kernal will only have derivatives defined up to $\\nu$. So, the parameter $\\nu$ is a smoothness parameter. For $\\nu < 1$, the first derivatives are not defined, and the functions drawn out of a GP with a Matérn kernel with $\\nu < 1$ are therefore very rough.\n",
    "\n",
    "So, how do we interpret all of this? For ease of parsing the statements that follow, it may be useful to have the mathematical functions for the SE kernel in mind. If $\\mathbf{x}$ and $\\mathbf{x}'$ are close to each other, the kernel returns a large value. The value returned by the kernel falls off as $\\mathbf{x}$ and $\\mathbf{x}'$ grow farther apart. So, the covariance between $\\mathbf{x}$ and $\\mathbf{x}'$ is large if they are close, and small if they are farther apart. A large covariance means that $f(\\mathbf{x})$ and $f(\\mathbf{x}')$ should be close to one another, and a small covariance means that they are unrelated.\n",
    "\n",
    "Finally, we note that each of the kernels have parameters. In all of the kernels listed above, there are the **marginal standard deviation** $\\alpha$ and the **length scale** $\\rho$. The Matérn kernel also has the smoothness parameter $\\nu$ and the periodic kernel also has the period $T$. Other kernels not listed above may have other parameters. So, in this sense, this \"nonparametric\" model has some tunable parameters. Specifically, these parameters say something about how the possible functions $f(\\mathbf{x})$ might behave. In the examples above, the covariance is modulated by $\\rho$. If $\\rho$ is large, then $\\mathbf{x}$ and $\\mathbf{x}'$ do not have to be so close together to influence each other. This means that the function is not so rapidly varying. So, $\\rho$ sets a length scale over which the function $f(\\mathbf{x})$ varies. Similarly, $\\alpha$ sets the amplitude of the variations. The larger $\\alpha$ is, the more $f(\\mathbf{x})$ will vary in the vertical direction. The parameters $\\theta_k$ of the kernel are called **hyperparameters**, because their inclusion results in a hierarchical Bayesian model. Specifically, we can write Bayes's theorem for a model with a GP prior for $f$ with zero mean function as\n",
    "\n",
    "\\begin{align}\n",
    "\\pi(f, \\theta_k \\mid \\mathbf{y}, \\mathbf{X}) = \\frac{\\pi(\\mathbf{y}, \\mathbf{X} \\mid f)\\,\\pi(f \\mid \\theta_k)\\,\\pi(\\theta_k)}{\\pi(\\mathbf{y}, \\mathbf{X})},\n",
    "\\end{align}\n",
    "\n",
    "where\n",
    "\n",
    "\\begin{align}\n",
    "f \\mid \\theta_k \\sim \\text{GP}(0, k(\\mathbf{x}, \\mathbf{x}'; \\theta_k)).\n",
    "\\end{align}\n",
    "\n",
    "In summary, the kernel specifies key features of the functions we are using to describe our data. It sets lengths scales for typical variation in the horizontal and vertical directions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sampling out of a Gaussian process prior\n",
    "\n",
    "We would like to draw samples out of a Gaussian process (in this case with an SE kernel). As an example, let's sample the function $f$ at discrete points out of the Gaussian process\n",
    "\n",
    "\\begin{align}\n",
    "f \\mid \\alpha, \\rho \\sim \\text{GP}(\\mathbf{0}, k_\\mathrm{SE}(\\mathbf{x}, \\mathbf{x}'; \\alpha, \\rho)).\n",
    "\\end{align}\n",
    "\n",
    "Remember that we can represent the prior $\\pi(f\\mid \\alpha, \\rho)$ as a multivariate Normal distribution over a set of finite points. For ease, let's consider a one-dimensional dependent variable, so $\\mathbf{x} = x$. Say we want to evaluate $f(x)$ at a set of positions $\\mathbf{x}_*$. Then the latent variables are distributed as\n",
    "\n",
    "\\begin{align}\n",
    "f(\\mathbf{x}_*) \\mid \\alpha, \\rho \\sim \\text{MultiNorm}(\\mathbf{0}, \\mathsf{K}(\\mathbf{x}_*, \\mathbf{x}_*)).\n",
    "\\end{align}\n",
    "\n",
    "To sample out of a Gaussian process, we compute the matrix $\\mathsf{K}(\\mathbf{x}_*, \\mathbf{x}_*)$ for given values of $\\alpha$ and $\\rho$, and then we sample out of this multivariate Normal distribution. I will demonstrate how this is done in both Numpy and Stan."
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sampling out of a GP prior using Stan\n",
    "\n",
    "Code to generate samples out of a GP prior using Stan is shown below. I will first display the code and then comment on its contents.\n",
    "\n",
    "```stan\n",
    "data {\n",
    "  // Data points\n",
    "  int<lower=1> Nstar;\n",
    "  array[Nstar] real xstar;\n",
    "\n",
    "  // Fixed marginal deviance and length scale\n",
    "  real alpha;\n",
    "  real rho;\n",
    "}\n",
    "\n",
    "\n",
    "transformed data {\n",
    "  // Covariance matrix\n",
    "  matrix[Nstar, Nstar] cov = gp_exp_quad_cov(xstar, alpha, rho) \n",
    "      + diag_matrix(rep_vector(1e-8, Nstar));\n",
    "\n",
    "  // Better to use Cholesky decomposition\n",
    "  matrix[Nstar, Nstar] L = cholesky_decompose(cov);\n",
    "}\n",
    "\n",
    "\n",
    "generated quantities {\n",
    "  // Draw from multivariate normal parametrized with Cholesky decomposition\n",
    "  vector[Nstar] f = multi_normal_cholesky_rng(rep_vector(0.0, Nstar), L);\n",
    "}\n",
    "```\n",
    "\n",
    "For this Stan code, we are fixing $\\alpha$ and $\\rho$. In a prior predictive check for a GP model, we would draw $\\alpha$ and $\\rho$ from their respective hyperpriors, but for this demonstration of sampling out of a GP prior, we will fix them.\n",
    "\n",
    "In setting up the covariance matrix based on the SE (exponentiated quadratic in Stan-speak) kernel, we can use Stan's built-in `gp_cov_exp_quad()` function. When we build the matrix, we add a small number to the diagonal purely for numerical stability. (Note that in Stan, `rep_vector()` makes a vector with repeated values, in this case `1e-8`.) Next, because it is generally more stable to work with Cholesky decompositions, we compute the Cholesky decomposition `L` of the covariance matrix. Finally, in the `generated quantities` block, we make our draws out of a multivariate Normal centered on zero (remember, we are considering centered and scaled data).\n",
    "\n",
    "Let's take a couple samples out of the prior and plot the results."
   ]
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   "execution_count": 8,
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      "text/plain": [
       "chain 1 |          | 00:00 Status"
      ]
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     "output_type": "display_data"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                \n"
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   "source": [
    "# Parameters for GP prior\n",
    "Nstar = 250\n",
    "xstar = np.linspace(-5, 5, Nstar)\n",
    "alpha = 1.0\n",
    "rho = 1.0\n",
    "data = dict(Nstar=Nstar, xstar=xstar, alpha=alpha, rho=rho)\n",
    "\n",
    "# Compile and sample!\n",
    "with bebi103.stan.disable_logging():\n",
    "    sm_prior = cmdstanpy.CmdStanModel(stan_file=\"gp_prior_fixed_rho_alpha.stan\")\n",
    "\n",
    "    samples = sm_prior.sample(\n",
    "        data=data,\n",
    "        iter_sampling=1,\n",
    "        fixed_param=True,\n",
    "        chains=1,\n",
    "    )\n",
    "    \n",
    "samples = az.from_cmdstanpy(posterior=samples, prior=samples, prior_predictive=\"f\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the result of our sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "  const docs_json = {\"0b37eeb4-ce36-4742-83c0-feb7ee7d7786\":{\"version\":\"3.3.0\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p1438\",\"attributes\":{\"x_range\":{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p1447\",\"attributes\":{\"start\":-5,\"end\":5}},\"y_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p1440\"},\"x_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1448\"},\"y_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1449\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1445\"},\"renderers\":[{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1473\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1467\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1468\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1469\"},\"data\":{\"type\":\"map\",\"entries\":[[\"x\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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\"},\"shape\":[250],\"dtype\":\"float64\",\"order\":\"little\"}],[\"y\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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":\"p1477\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"\\u03b1 = 1, \\u03c1 = 1\"},\"renderers\":[{\"id\":\"p1473\"}]}}]}}],\"frame_width\":450,\"frame_height\":250}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"toggle_value1\",\"properties\":[{\"name\":\"active_icons\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"options\",\"kind\":\"Any\",\"default\":{\"type\":\"map\",\"entries\":[[\"favorite\",\"heart\"]]}},{\"name\":\"value\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"_reactions\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"_base_url\",\"kind\":\"Any\",\"default\":\"https://tabler-icons.io/static/tabler-icons/icons/\"}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"value\",\"kind\":\"Any\",\"default\":null},{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
       "  const render_items = [{\"docid\":\"0b37eeb4-ce36-4742-83c0-feb7ee7d7786\",\"roots\":{\"p1438\":\"df4cfbf9-5172-4e29-b69a-46e2d122b918\"},\"root_ids\":[\"p1438\"]}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1438"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the result\n",
    "p = bokeh.plotting.figure(\n",
    "    frame_height=250,\n",
    "    frame_width=450,\n",
    "    x_axis_label=\"x\",\n",
    "    y_axis_label=\"f(x)\",\n",
    "    x_range=[-5, 5],\n",
    ")\n",
    "\n",
    "p.circle(\n",
    "    xstar, samples.prior_predictive[\"f\"].values.flatten(), legend_label=\"α = 1, ρ = 1\"\n",
    ")\n",
    "\n",
    "bokeh.io.show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have plotted the function as points to emphasize that we are evaluating the function $f(x)$ at discrete points. Note that the function varies over a length scale of approximately 1 and the amplitude is also approximately 1. We can make the function vary more rapidly by tuning $\\rho$ down."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4d8bb03ce9f040179bec7cbcb2811c76",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "chain 1 |          | 00:00 Status"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                \n"
     ]
    },
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       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
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       "  if (root.Bokeh !== undefined) {\n",
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       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
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       "      }\n",
       "    }, 10, root)\n",
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      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1438"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[\"rho\"] = 0.5\n",
    "\n",
    "with bebi103.stan.disable_logging():\n",
    "    samples = sm_prior.sample(data=data, iter_sampling=1, fixed_param=True, chains=1)\n",
    "\n",
    "samples = az.from_cmdstanpy(prior=samples, prior_predictive=\"f\")\n",
    "\n",
    "p.circle(\n",
    "    xstar,\n",
    "    samples.prior_predictive[\"f\"].values.flatten(),\n",
    "    color=\"orange\",\n",
    "    legend_label=\"α = 1, ρ = 0.5\",\n",
    ")\n",
    "\n",
    "bokeh.io.show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, different hyperparameter values yield different kinds of functions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sampling out of a GP prior using Numpy\n",
    "\n",
    "At the heart of being able to sample out of a GP prior (and indeed all sampling with GP-based models) is the ability to compute the covariance matrix from for a given kernel and $x$-points. In Stan, only covariance matrices computed using SE kernels are built-in; you can always code up your own function for Stan to use to build covariance matrices for whatever kernel you please. Conveniently, the `bebi103.gp` module has functions to compute the covariance matrices for all of the kernels listed above. To demonstrate sampling out of a GP prior with Numpy, let's use the Matérn kernel with $\\nu = 3/2$. The resulting functions are only once differentiable and will therefore be much rougher than what we got with the SE kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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bject\",\"name\":\"Legend\",\"id\":\"p1476\",\"attributes\":{\"items\":[{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p1477\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"\\u03b1 = 1, \\u03c1 = 1\"},\"renderers\":[{\"id\":\"p1473\"}]}},{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p1489\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"\\u03b1 = 1, \\u03c1 = 0.5\"},\"renderers\":[{\"id\":\"p1486\"}]}},{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p1503\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"\\u03b1 = 1, \\u03c1 = 1, Mat\\u00e9rn\"},\"renderers\":[{\"id\":\"p1500\"}]}}]}}],\"frame_width\":450,\"frame_height\":250}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"toggle_value1\",\"properties\":[{\"name\":\"active_icons\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"options\",\"kind\":\"Any\",\"default\":{\"type\":\"map\",\"entries\":[[\"favorite\",\"heart\"]]}},{\"name\":\"value\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"_reactions\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"_base_url\",\"kind\":\"Any\",\"default\":\"https://tabler-icons.io/static/tabler-icons/icons/\"}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"value\",\"kind\":\"Any\",\"default\":null},{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
       "  const render_items = [{\"docid\":\"2823de64-74a6-40ce-85dc-9570b515ca76\",\"roots\":{\"p1438\":\"b60c026d-4d26-4a77-8fe3-c3e57b0301b9\"},\"root_ids\":[\"p1438\"]}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1438"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Again use rho = 1\n",
    "rho = 1.0\n",
    "\n",
    "# Make covariance matrix\n",
    "K = bebi103.gp.cov_matern(xstar, alpha=alpha, rho=rho, nu=1.5)\n",
    "\n",
    "# Sample out of multivariate normal\n",
    "rg = np.random.default_rng()\n",
    "f = rg.multivariate_normal(np.zeros_like(xstar), K)\n",
    "\n",
    "# Add the result to plot\n",
    "p.circle(\n",
    "    xstar, f, color=\"tomato\", legend_label=\"α = 1, ρ = 1, Matérn\",\n",
    ")\n",
    "\n",
    "bokeh.io.show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since it is convenient to sample using Numpy, let's make a **spaghetti plot**, which is a good way to visualize the kind of functions you will get by sampling out of a GP prior. We will again use an SE kernel with $\\alpha = \\rho = 1$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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gle_value1\",\"properties\":[{\"name\":\"active_icons\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"options\",\"kind\":\"Any\",\"default\":{\"type\":\"map\",\"entries\":[[\"favorite\",\"heart\"]]}},{\"name\":\"value\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"_reactions\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"_base_url\",\"kind\":\"Any\",\"default\":\"https://tabler-icons.io/static/tabler-icons/icons/\"}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"value\",\"kind\":\"Any\",\"default\":null},{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
       "  const render_items = [{\"docid\":\"a1be5d6d-68da-4f17-ad94-4876f32ab6cc\",\"roots\":{\"p1510\":\"c4bafb32-b550-4a6c-bb06-98fa1ad9ffac\"},\"root_ids\":[\"p1510\"]}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1510"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make covariance matrix\n",
    "K = bebi103.gp.cov_exp_quad(xstar, alpha=1.0, rho=1.0)\n",
    "\n",
    "# Sample out of multivariate normal\n",
    "f = [rg.multivariate_normal(np.zeros_like(xstar), K) for _ in range(100)]\n",
    "\n",
    "# Make plot\n",
    "p = bokeh.plotting.figure(\n",
    "    frame_height=250,\n",
    "    frame_width=450,\n",
    "    x_axis_label=\"x\",\n",
    "    y_axis_label=\"f(x)\",\n",
    "    x_range=[-5, 5],\n",
    ")\n",
    "p.multi_line([xstar for _ in range(100)], f, line_width=0.5)\n",
    "\n",
    "bokeh.io.show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Composing kernels\n",
    "\n",
    "We may take any of the above kernels and compose them by multiplying or adding them together. For example, say we think our function is locally periodic, but may nonetheless vary over time. We might compose a kernel that is the product of a periodic and SE kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mult_kern(x1, x2, alpha, rho_SE, rho_per, T):\n",
    "    \"\"\"Kernel formed from multiplying periodic and SE kernels \"\"\"\n",
    "    per = bebi103.gp.periodic_kernel(x1, x2, 1.0, rho_per, T)\n",
    "    se = bebi103.gp.se_kernel(x1, x2, alpha, rho_SE)\n",
    "    \n",
    "    return se * per"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use this kernel to build a covariance matrix and draw a sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"d86f4f50-c272-4d1c-9bd9-d719202a71d7\" data-root-id=\"p1748\" style=\"display: contents;\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "  const docs_json = {\"183aa360-56cc-4952-84a2-58d71f25c40a\":{\"version\":\"3.3.0\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p1748\",\"attributes\":{\"x_range\":{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p1757\",\"attributes\":{\"start\":-5,\"end\":5}},\"y_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p1750\"},\"x_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1758\"},\"y_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1759\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1755\"},\"renderers\":[{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1783\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1777\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1778\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1779\"},\"data\":{\"type\":\"map\",\"entries\":[[\"x\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"AAAAAAAAFMAVTOYg4NYTwCmYzEHArRPAPuSyYqCEE8BSMJmDgFsTwGd8f6RgMhPAe8hlxUAJE8CQFEzmIOASwKRgMgcBtxLAuawYKOGNEsDO+P5IwWQSwOJE5WmhOxLA95DLioESEsAL3bGrYekRwCApmMxBwBHANHV+7SGXEcBJwWQOAm4RwF4NSy/iRBHAclkxUMIbEcCHpRdxovIQwJvx/ZGCyRDAsD3ksmKgEMDEicrTQncQwNnVsPQiThDA7iGXFQMlEMAE3PpsxvcPwC10x66GpQ/AVgyU8EZTD8CApGAyBwEPwKg8LXTHrg7A0tT5tYdcDsD7bMb3RwoOwCQFkzkIuA3ATZ1fe8hlDcB2NSy9iBMNwKDN+P5IwQzAyGXFQAlvDMDy/ZGCyRwMwBuWXsSJygvARC4rBkp4C8BtxvdHCiYLwJZexInK0wrAv/aQy4qBCsDojl0NSy8KwBInKk8L3QnAOr/2kMuKCcBkV8PSizgJwI3vjxRM5gjAtodcVgyUCMDfHymYzEEIwAi49dmM7wfAMVDCG02dB8Ba6I5dDUsHwISAW5/N+AbArRgo4Y2mBsDWsPQiTlQGwP9IwWQOAgbAKOGNps6vBcBReVrojl0FwHoRJypPCwXApKnzaw+5BMDNQcCtz2YEwPbZjO+PFATAH3JZMVDCA8BICiZzEHADwHGi8rTQHQPAmjq/9pDLAsDD0os4UXkCwOxqWHoRJwLAFgMlvNHUAcA/m/H9kYIBwGgzvj9SMAHAkcuKgRLeAMC6Y1fD0osAwOP7IwWTOQDAGCjhjabO/79sWHoRJyr/v76IE5Wnhf6/ELmsGCjh/b9i6UWcqDz9v7QZ3x8pmPy/Bkp4o6nz+79YehEnKk/7v6qqqqqqqvq//NpDLisG+r9QC92xq2H5v6I7djUsvfi/9GsPuawY+L9GnKg8LXT3v5jMQcCtz/a/6vzaQy4r9r88LXTHrob1v5BdDUsv4vS/4o2mzq899L80vj9SMJnzv4bu2NWw9PK/2B5yWTFQ8r8qTwvdsavxv3x/pGAyB/G/zq895LJi8L9AwK3PZnzvv+gg4NZnM+6/iIES3mjq7L8w4kTlaaHrv9BCd+xqWOq/eKOp82sP6b8gBNz6bMbnv8BkDgJufea/aMVACW805b8IJnMQcOvjv7CGpRdxouK/UOfXHnJZ4b/4RwomcxDgv0BReVrojt2/gBLeaOr82r/Q00J37GrYvxCVp4Xu2NW/YFYMlPBG07+gF3Gi8rTQv+Cxq2HpRcy/gDR1fu0hx78Atz6b8f3Bv0BzEHDrs7m/gPBGU+fXrr8A9tmM74+UvwD22Yzvj5Q/gPBGU+fXrj9AcxBw67O5PwC3Ppvx/cE/YDR1fu0hxz/gsath6UXMP6AXcaLytNA/YFYMlPBG0z8QlaeF7tjVP9DTQnfsatg/gBLeaOr82j9AUXla6I7dP/hHCiZzEOA/UOfXHnJZ4T+whqUXcaLiPwgmcxBw6+M/aMVACW805T/AZA4Cbn3mPyAE3Ppsxuc/eKOp82sP6T/QQnfsaljqPzDiROVpoes/iIES3mjq7D/oIODWZzPuP0DArc9mfO8/0K895LJi8D98f6RgMgfxPyhPC92xq/E/2B5yWTFQ8j+E7tjVsPTyPzS+P1IwmfM/4I2mzq899D+QXQ1LL+L0PzwtdMeuhvU/7PzaQy4r9j+YzEHArc/2P0ScqDwtdPc/9GsPuawY+D+gO3Y1LL34P1AL3bGrYfk//NpDLisG+j+sqqqqqqr6P1h6EScqT/s/CEp4o6nz+z+0Gd8fKZj8P2DpRZyoPP0/ELmsGCjh/T+8iBOVp4X+P2xYehEnKv8/GCjhjabO/z/k+yMFkzkAQLpjV8PSiwBAkMuKgRLeAEBoM74/UjABQD6b8f2RggFAFgMlvNHUAUDsalh6EScCQMTSizhReQJAmjq/9pDLAkBwovK00B0DQEgKJnMQcANAHnJZMVDCA0D22YzvjxQEQMxBwK3PZgRApKnzaw+5BEB6EScqTwsFQFJ5WuiOXQVAKOGNps6vBUD+SMFkDgIGQNaw9CJOVAZArBgo4Y2mBkCEgFufzfgGQFrojl0NSwdAMlDCG02dB0AIuPXZjO8HQOAfKZjMQQhAtIdcVgyUCECM748UTOYIQGRXw9KLOAlAPL/2kMuKCUAQJypPC90JQOiOXQ1LLwpAwPaQy4qBCkCYXsSJytMKQGzG90cKJgtARC4rBkp4C0Acll7EicoLQPD9kYLJHAxAyGXFQAlvDECgzfj+SMEMQHg1LL2IEw1ATJ1fe8hlDUAkBZM5CLgNQPxsxvdHCg5A0NT5tYdcDkCoPC10x64OQICkYDIHAQ9AWAyU8EZTD0AsdMeuhqUPQATc+mzG9w9A7iGXFQMlEEDY1bD0Ik4QQMSJytNCdxBAsD3ksmKgEECc8f2RgskQQIalF3Gi8hBAclkxUMIbEUBeDUsv4kQRQEjBZA4CbhFANHV+7SGXEUAgKZjMQcARQAzdsath6RFA9pDLioESEkDiROVpoTsSQM74/kjBZBJAuKwYKOGNEkCkYDIHAbcSQJAUTOYg4BJAfMhlxUAJE0BmfH+kYDITQFIwmYOAWxNAPuSyYqCEE0AqmMxBwK0TQBRM5iDg1hNAAAAAAAAAFEA=\"},\"shape\":[250],\"dtype\":\"float64\",\"order\":\"little\"}],[\"y\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"toggle_value1\",\"properties\":[{\"name\":\"active_icons\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"options\",\"kind\":\"Any\",\"default\":{\"type\":\"map\",\"entries\":[[\"favorite\",\"heart\"]]}},{\"name\":\"value\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"_reactions\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"_base_url\",\"kind\":\"Any\",\"default\":\"https://tabler-icons.io/static/tabler-icons/icons/\"}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"value\",\"kind\":\"Any\",\"default\":null},{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
       "  const render_items = [{\"docid\":\"183aa360-56cc-4952-84a2-58d71f25c40a\",\"roots\":{\"p1748\":\"d86f4f50-c272-4d1c-9bd9-d719202a71d7\"},\"root_ids\":[\"p1748\"]}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1748"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = dict(alpha=1, rho_SE=3, rho_per=1, T=2)\n",
    "K = bebi103.gp.cov_from_kernel(xstar, xstar, mult_kern, **params)\n",
    "\n",
    "f = rg.multivariate_normal(np.zeros_like(xstar), K)\n",
    "\n",
    "# Make plot\n",
    "p = bokeh.plotting.figure(\n",
    "    frame_height=250,\n",
    "    frame_width=450,\n",
    "    x_axis_label=\"x\",\n",
    "    y_axis_label=\"f(x)\",\n",
    "    x_range=[-5, 5],\n",
    ")\n",
    "p.line(xstar, f, line_width=2)\n",
    "\n",
    "bokeh.io.show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, we can add kernels together. Say we have a periodic function, but it may increase over time. We can get a kernel that captures this behavior by adding a linear and a periodic kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"c3cc6393-3949-472e-b0e7-cfa4f1042753\" data-root-id=\"p1788\" style=\"display: contents;\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "  const docs_json = {\"73f1366f-2596-418c-b190-769fcc9e0a2a\":{\"version\":\"3.3.0\",\"title\":\"Bokeh 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":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"toggle_value1\",\"properties\":[{\"name\":\"active_icons\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"options\",\"kind\":\"Any\",\"default\":{\"type\":\"map\",\"entries\":[[\"favorite\",\"heart\"]]}},{\"name\":\"value\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"_reactions\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"_base_url\",\"kind\":\"Any\",\"default\":\"https://tabler-icons.io/static/tabler-icons/icons/\"}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"value\",\"kind\":\"Any\",\"default\":null},{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
       "  const render_items = [{\"docid\":\"73f1366f-2596-418c-b190-769fcc9e0a2a\",\"roots\":{\"p1788\":\"c3cc6393-3949-472e-b0e7-cfa4f1042753\"},\"root_ids\":[\"p1788\"]}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1788"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def add_kern(x1, x2, sigma_b, sigma, alpha, rho, T):\n",
    "    \"\"\"Kernel formed from adding linear and SE kernels \"\"\"\n",
    "    per = bebi103.gp.periodic_kernel(x1, x2, alpha, rho, T)\n",
    "    linear = bebi103.gp.linear_kernel(x1, x2, sigma_b, sigma)\n",
    "    \n",
    "    return linear + per\n",
    "\n",
    "\n",
    "params = dict(sigma_b=0, sigma=1, alpha=1, rho=1, T=2)\n",
    "K = bebi103.gp.cov_from_kernel(xstar, xstar, add_kern, **params)\n",
    "\n",
    "f = rg.multivariate_normal(np.zeros_like(xstar), K)\n",
    "\n",
    "# Make plot\n",
    "p = bokeh.plotting.figure(\n",
    "    frame_height=250,\n",
    "    frame_width=450,\n",
    "    x_axis_label=\"x\",\n",
    "    y_axis_label=\"f(x)\",\n",
    "    x_range=[-5, 5],\n",
    ")\n",
    "p.line(xstar, f, line_width=2)\n",
    "\n",
    "bokeh.io.show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inference with GPs\n",
    "\n",
    "Gaussian processes can be used as priors in many models. For example, imagine we are measuring the number of gene transcripts in cells by RNA FISH. We assume the expression is not bursty and well-described by a Poisson distribution parametrized by rate parameter $\\lambda$. But we do this experiment for various concentrations $\\mathbf{c} = \\{c_1, c_2, \\ldots\\}$ of IPTG, which can affect gene expression levels. We may not know how it affects those levels, so we can model $\\lambda$ using a Gaussian process with a SE kernel. Our statistical model is then\n",
    "\n",
    "\\begin{align}\n",
    "&\\ln \\lambda(\\mathbf{c}) \\mid \\alpha, \\rho \\sim\\text{GP}(0, k_\\mathrm{SE}(c, c'; \\alpha, \\rho)), \\\\[1em]\n",
    "&n_i \\sim \\text{Pois}(\\lambda(c_i)) \\;\\forall i.\n",
    "\\end{align}\n",
    "\n",
    "Of course, we do need to specify hyperpriors for $\\alpha$ and $\\rho$ to complete the model. Note that the mean function here is not zero, since we cannot center and scale count data the same way we can continuous data.\n",
    "\n",
    "In practice, we consider a finite set of points, so we could construct a covariance matrix $\\mathsf{K}(\\mathbf{c}, \\mathbf{c})$ from the kernel. The entries of $\\mathsf{K}$ are \n",
    "\n",
    "\\begin{align}\n",
    "K_{ij} = k_\\mathrm{SE}(c_i, c_j ; \\alpha, \\rho).\n",
    "\\end{align}\n",
    "\n",
    "We then could specify our model as\n",
    "\n",
    "\\begin{align}\n",
    "&\\alpha \\sim \\text{appropriate hyperprior}\\\\[1em]\n",
    "&\\rho \\sim \\text{appropriate hyperprior}\\\\[1em]\n",
    "&\\ln \\lambda(\\mathbf{c}) \\mid \\alpha, \\rho \\sim \\text{MultiNormal}(\\mathbf{0}, \\mathsf{K}) \\\\[1em]\n",
    "&n_i \\sim \\text{Pois}(\\lambda(c_i)) \\;\\forall i.\n",
    "\\end{align}\n",
    "\n",
    "We will discuss inference with this model in a subsequent lesson on GPs in practice. \n",
    "\n",
    "Now, we will consider the special case where we have a Normal likelihood with a GP prior. In this case, we can make significant analytical progress that greatly eases the analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normal likelihoods with Gaussian process priors\n",
    "\n",
    "While GPs priors can be used for arbitrary likelihoods, such as the Poisson likelihood we used above, Normal likelihoods are widely encountered and have some special (and *very* convenient) properties. Specifically, we expect our observation $y$ to follow $y = f(\\mathbf{x})$, with some Normally distributed, possibly heteroscedastic error $\\boldsymbol{\\sigma}$, i.e.,\n",
    "\n",
    "\\begin{align}\n",
    "y_i \\sim \\text{Norm}(f(\\mathbf{x}_i), \\sigma_i) \\;\\;\\forall i.\n",
    "\\end{align}\n",
    "\n",
    "I will not prove the very convenient properties of this likelihood here, but will state them:\n",
    "\n",
    "1. For a given set of hyperparameters, a GP prior is conjugate to a Normal likelihood. This means that the posterior is known analytically.\n",
    "2. The nonparametric function $f(\\mathbf{x})$ may be marginalized out. The resulting inference then only involves the hyperparameters of the kernel.\n",
    "\n",
    "In our demonstrations, we will use an arbitrary kernel, $k(\\mathbf{x}, \\mathbf{x}';\\theta_k)$, since the results we will show are independent of choice of kernel (provided of course that it is positive definite). The complete model is then (without yet specifying priors for the hyperparameters)\n",
    "\n",
    "\\begin{align}\n",
    "&\\theta_k \\sim \\text{to be defined}, \\\\[1em]\n",
    "&\\boldsymbol{\\sigma} \\sim \\text{to be defined},\\\\[1em]\n",
    "&f \\sim \\text{GP}(\\mathbf{0}, k(\\mathbf{x}, \\mathbf{x}';\\theta_k)),\\\\[1em]\n",
    "&y_i \\sim \\text{Norm}(f(\\mathbf{x}_i), \\sigma_i) \\;\\;\\forall i.\n",
    "\\end{align}\n",
    "\n",
    "Because of the conjugacy, the posterior is also a GP,\n",
    "\n",
    "\\begin{align}\n",
    "f(\\mathbf{x}) \\mid \\mathbf{y}, \\mathbf{X} \\sim \\text{GP}(m(\\mathbf{x}), k(\\mathbf{x}, \\mathbf{x}'; \\theta_k) + \\sigma_\\mathbf{x}^2\\,\\delta_{\\mathbf{x}, \\mathbf{x}'}),\n",
    "\\end{align}\n",
    "\n",
    "with\n",
    "\n",
    "\\begin{align}\n",
    "\\delta_{\\mathbf{x},\\mathbf{x}'} = \\left\\{\\begin{array}{lll}1 && \\mathbf{x} = \\mathbf{x}'\\\\\n",
    "0 && \\mathbf{x} \\ne \\mathbf{x}',\\end{array}\\right.\n",
    "\\end{align}\n",
    "\n",
    "and $\\sigma_\\mathbf{x}^2$ is defined as the variance in the Normal likelihood at point $\\mathbf{x}$. The posterior kernel is given by the prior kernel plus the homoscedastic variance. (In the heteroscedastic case, the respective variance is added to each point $\\mathbf{x}$ where the function $f(\\mathbf{x})$ is evaluated.) We have defined a posterior mean function $m(\\mathbf{x})$, which depends on the measured quantities $\\mathbf{y}$. We will show the analytical expression for $m(\\mathbf{x})$ evaluated at specific points in terms of the covariance matrix derived from the kernel momentarily in the next section. Note that a GP prior is *only* conjugate to a Normal likelihood and not to other likelihoods.\n",
    "\n",
    "But wait, there's more! Through some mathematical grunge, we may marginalize away $f$ out of this model (again, this is a special case of a Normal likelihood; we cannot do this in general). Skipping the derivation, the resulting model is\n",
    "\n",
    "\\begin{align}\n",
    "&\\theta_k \\sim \\text{to be defined}, \\\\[1em]\n",
    "&\\boldsymbol{\\sigma} \\sim \\text{to be defined},\\\\[1em]\n",
    "&\\mathbf{y} \\sim \\text{MultiNorm}(\\mathbf{0}, \\mathsf{K}_\\mathbf{y}),\n",
    "\\end{align} \n",
    "\n",
    "where we have defined\n",
    "\n",
    "\\begin{align}\n",
    "\\mathsf{K}_\\mathbf{y} \\equiv \\mathsf{K}(\\mathbf{X}, \\mathbf{X}) + \\mathrm{diag}(\\boldsymbol{\\sigma}^2),\n",
    "\\end{align}\n",
    "\n",
    "where $\\mathrm{diag}(\\boldsymbol{\\sigma})$ is a diagonal covariance matrix constructed from the array of $\\sigma$ values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The posterior predictive distribution of function values\n",
    "\n",
    "If we specify priors for the hyperparameters $\\theta_k$ and for $\\sigma$, we have a fully specified model. Because we can conveniently marginalize out $f$, we only need to focus on $\\theta_k$ and $\\boldsymbol{\\sigma}$ as parameters to infer. In the next lesson, we will learn how to find $\\theta_k$ and $\\sigma$ by finding the MAP and (better) how to draw samples of them using MCMC with Stan. For now, we will focus on understanding the posterior distribution for *particular* values of $\\theta_k$ and $\\boldsymbol{\\sigma}$. \n",
    "\n",
    "Going from the posterior samples of the parameters to the posterior predictive distribution is kind of tricky because the values of the function $f$ at unmeasured positions $\\mathbf{X}_*$, which we shall denote for notational convenience as $\\mathbf{f}_* = f(\\mathbf{X}_*)$, depend on the observed values $\\mathbf{y}$ and the positions $\\mathbf{X}$ at which $\\mathbf{y}$ were measured. We wish to compute the posterior predictive distribution of function values, $\\mathbf{f}_* \\mid \\mathbf{X}_*, \\mathbf{X}, \\mathbf{y}$. We can take advantage of some properties of multivariate Normal distributions to do it.\n",
    "\n",
    "To start, we can specify a *joint* distribution of the posterior of the observed points, $\\mathbf{y} = f(\\mathbf{X})$ and the desired set of function evaluations, $\\mathbf{f}_* = f(\\mathbf{X}_*)$. That is, the joint distribution $\\mathbf{y}, \\mathbf{f}_* \\mid \\mathbf{X}_*, \\mathbf{X}$. We can do this because we know the posterior is a Gaussian process. The joint distribution is again a multivariate Gaussian,\n",
    "\n",
    "\\begin{align}\n",
    "\\begin{pmatrix}\n",
    "\\mathbf{y}\\\\\n",
    "\\mathbf{f}_*\n",
    "\\end{pmatrix} \\sim\n",
    "\\text{MultiNorm}\\left( \\mathbf{0},\n",
    "\\begin{pmatrix}\n",
    "\\mathsf{K}_\\mathbf{y} & \\mathsf{K}_* \\\\[0.5em]\n",
    "\\mathsf{K}_*^\\mathsf{T} & \\mathsf{K}_{**}\n",
    "\\end{pmatrix}\n",
    "\\right).\n",
    "\\end{align}\n",
    "\n",
    "For further notational simplicity, we have defined\n",
    "\n",
    "\\begin{align}\n",
    "&\\mathsf{K}_* = \\mathsf{K}(\\mathbf{X}, \\mathbf{X}_*) \\\\[1em]\n",
    "&\\mathsf{K}_{**} = \\mathsf{K}(\\mathbf{X}_*, \\mathbf{X}_*).\n",
    "\\end{align}\n",
    "\n",
    "It is a property of Gaussian matrices (which may be derived with lots of grunge) that we can get the conditional distribution (the posterior predictive distribution) from the joint distribution. Using this result (again, not derived here), we can write the posterior predictive distribution as\n",
    "\n",
    "\\begin{align}\n",
    "\\mathbf{f}_* \\mid \\mathbf{X}_*, \\mathbf{X}, \\mathbf{y} \\sim\n",
    "\\text{MultiNorm}(\\mathbf{m}_*, \\mathsf{\\Sigma}_*),\n",
    "\\end{align}\n",
    "\n",
    "where\n",
    "\n",
    "\\begin{align}\n",
    "&\\mathbf{m}_* = \\mathsf{K}_*^\\mathsf{T} \\cdot \\mathsf{K}_\\mathbf{y}^{-1} \\cdot \\mathbf{y},\\\\\n",
    "&\\mathsf{\\Sigma}_* = \\mathsf{K}_{**} - \\mathsf{K}_*^\\mathsf{T} \\cdot \\mathsf{K}_\\mathbf{y}^{-1} \\cdot \\mathsf{K}_*.\n",
    "\\end{align}\n",
    "\n",
    "The vector $\\mathbf{m}_*$ gives the mean value of $\\mathbf{f}_* \\equiv f(\\mathbf{X}_*)$. The diagonal entries of $\\mathsf{\\Sigma}_*$ give the variance, and therefore the uncertainty, in $\\mathbf{y}_*$. Computing $\\mathbf{m}_*$ and $\\mathsf{\\Sigma}_*$ (or at least its diagonal entries) gives us our predictions for a given set of hyperparameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Computing the parameters of the posterior predictive distribution\n",
    "\n",
    "At the center the calculation of $\\mathbf{m}_*$ is inverting the matrix $\\mathsf{K}_\\mathbf{y}$. More specifically, we need to compute $\\mathsf{K}_\\mathbf{y}^{-1}\\mathbf{y}$, which is the solution $\\boldsymbol{\\xi}$ to\n",
    "\n",
    "\\begin{align}\n",
    "\\mathsf{K}_\\mathbf{y} \\cdot \\boldsymbol{\\xi} = \\mathbf{y}.\n",
    "\\end{align}\n",
    "\n",
    "Because $\\mathsf{K}_\\mathbf{y}$ is symmetric and positive definite by construction, it has a **Cholesky decomposition**, $\\mathsf{L}$, such that\n",
    "\n",
    "\\begin{align}\n",
    "\\mathsf{K}_\\mathbf{y} = \\mathsf{L}\\cdot\\mathsf{L}^\\mathsf{T}.\n",
    "\\end{align}\n",
    "\n",
    "The matrix $\\mathsf{L}$ is lower triangular. Importantly, it can be stably computed in order $n^3$ operations (for an $n\\times n$ $\\mathsf{K}_\\mathbf{y}$). Substituting in $\\mathsf{K}_\\mathbf{y}$'s Cholesky decomposition,\n",
    "\n",
    "\\begin{align}\n",
    "\\mathsf{L}\\cdot\\mathsf{L}^\\mathsf{T} \\cdot \\boldsymbol{\\xi} = \\mathbf{y}.\n",
    "\\end{align}\n",
    "\n",
    "Let $\\mathbf{z} = \\mathsf{L}^\\mathsf{T} \\cdot \\boldsymbol{\\xi}$. Then we can solve for $\\mathbf{z}$ by solving\n",
    "\n",
    "\\begin{align}\n",
    "\\mathsf{L}\\cdot \\mathbf{z} = \\mathbf{y}.\n",
    "\\end{align}\n",
    "\n",
    "This is a triangular system and is easily solved. Given $\\mathbf{z}$, we then solve for $\\boldsymbol{\\xi}$ by solving another triangular system\n",
    "\n",
    "\\begin{align}\n",
    "\\mathsf{L}^\\mathsf{T}\\cdot \\boldsymbol{\\xi} = \\mathbf{z}.\n",
    "\\end{align}\n",
    "\n",
    "Note that $\\mathsf{L}^\\mathsf{T}$ is an upper triangular matrix. Stan has built-in solvers for *lower* triangular matrices, so we can convert this to a lower triangular system by taking the transpose of both sides.\n",
    "\n",
    "\\begin{align}\n",
    "\\left(\\mathsf{L}^\\mathsf{T}\\cdot \\boldsymbol{\\xi}\\right)^\\mathsf{T} = \\boldsymbol{\\xi}^\\mathsf{T} \\mathsf{L} = \\mathbf{z}^\\mathsf{T}.\n",
    "\\end{align}\n",
    "\n",
    "So, we never directly invert the $\\mathsf{K}_\\mathbf{y}$ matrix. Given that we can compute $\\boldsymbol{\\xi}$, we then directly compute $\\mathbf{m}_* = \\mathsf{K}_*^\\mathsf{T}\\cdot \\boldsymbol{\\xi}$.\n",
    "\n",
    "Now, to compute the covariance matrix $\\mathsf{\\Sigma}_*$, we also need to compute $\\mathsf{\\Xi} = \\mathsf{K}_\\mathbf{y}^{-1}\\cdot \\mathsf{K}_*$. This is accomplished again by Cholesky decomposition.\n",
    "\n",
    "\\begin{align}\n",
    "& \\text{solve } \\mathsf{L}\\cdot \\mathsf{Z} = \\mathsf{K}_*, \\\\[1em]\n",
    "& \\text{solve } \\mathsf{\\Xi}^\\mathsf{T} \\cdot \\mathsf{L}  = \\mathsf{Z}.\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting an analytical posterior\n",
    "\n",
    "Following the above prescription, we can compute the posterior mean $\\mathbf{m}^*$ and the posterior covariance $\\mathsf{\\Sigma}_*$, from which we can plot our nonparametric function.\n",
    "\n",
    "Using the specific example of the rate constant versus temperature data, we can use a GP prior with a SE kernel. Our model, leaving the priors on the homoscedastic error $\\sigma$ and the kernel hyperparameters is\n",
    "\n",
    "\\begin{align}\n",
    "&\\alpha \\sim \\text{to be specified} \\\\[1em]\n",
    "&\\rho \\sim \\text{to be specified} \\\\[1em]\n",
    "&\\sigma \\sim \\text{to be specified} \\\\[1em]\n",
    "&\\mathbf{k}_\\mathrm{scaled} \\sim \\text{GP}(\\mathbf{0}, k_\\mathrm{SE}(\\mathbf{T}_\\mathrm{scaled}, \\mathbf{T}'_\\mathrm{scaled};\\alpha, \\rho) + \\delta_{\\mathbf{T}_\\mathrm{scaled}, \\mathbf{T}'_\\mathrm{scaled}}\\sigma^2).\n",
    "\\end{align}\n",
    "\n",
    "We have already marginalized out $\\mathbf{f}(\\mathbf{x})$. We can then directly go forward to give the posterior *for a particular choice of* $\\alpha$, $\\rho$, *and* $\\sigma$. We will choose $\\alpha = \\rho = \\sigma = 1$ for this illustration. This is a reasonable choice because centering and scaling aims to bring the variation in the data toward unity.\n",
    "\n",
    "We typically plot it as the mean function with a shaded region containing 95% of the probability mass. To get this 95% credible region, we directly use the diagonal terms of $\\mathsf{\\Sigma}$.\n",
    "\n",
    "To start, we center and scale the temperature and rate constants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Center and scale\n",
    "k_scaled = (k - k.mean()) / k.std()\n",
    "T_scaled = (T - T.mean()) / T.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we need to determine for which values of the temperature we want to evaluate our nonparametric function. This will be the $\\mathbf{x}_*$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sample at 250 points\n",
    "Nstar = 250\n",
    "\n",
    "# Set up xstar\n",
    "T_range = T_scaled.max() - T_scaled.min()\n",
    "xstar = np.linspace(\n",
    "    T_scaled.min() - 0.05 * T_range, T_scaled.max() + 0.05 * T_range, Nstar\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now comes the involved part of the calculation. For a given set of hyperparameters and $\\sigma$, we compute the posterior mean function $\\mathbf{m}_*$ and covariance matrix $\\mathsf{\\Sigma}_*$ using the linear algebra manipulations above. This is conveniently included in the `bebi103.gp.posterior_mean_cov()` function. It takes as parameters the (centered and scaled) x- and y- values of the measured data, the data points $\\mathbf{X}_*$ that we want the values of the nonparametric function, $\\sigma$, a kernel (default is the SE kernel) and the hyperparameters of the kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "mstar, Sigmastar = bebi103.gp.posterior_mean_cov(\n",
    "    T_scaled, k_scaled, xstar, sigma=1.0, rho=1.0, alpha=1.0\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have $\\mathbf{m}_*$ and $\\mathsf{\\Sigma}_*$, we can compute the upper and lower bounds of the credible interval. Because the posterior is a Gaussian process, we know that we can compute the 95% credible region from the diagonal terms of the covariance matrix $\\mathsf{\\Sigma}_*$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bound of credible interval\n",
    "high = mstar + 1.96 * np.sqrt(np.diag(Sigmastar))\n",
    "low = mstar - 1.96 * np.sqrt(np.diag(Sigmastar))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we just have to uncenter and unscale everything and we can make a plot!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
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       "  const render_items = [{\"docid\":\"79023f1d-28cc-44c8-8b5a-c14b106cf7af\",\"roots\":{\"p1828\":\"b6e3fdf1-8c1a-4dfa-ba8c-00439d6c2fa4\"},\"root_ids\":[\"p1828\"]}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1828"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Uncenter/scale\n",
    "Tstar = T.std() * xstar + T.mean()\n",
    "kstar = k.std() * mstar + k.mean()\n",
    "low = k.std() * low + k.mean()\n",
    "high = k.std() * high + k.mean()\n",
    "\n",
    "p = bokeh.plotting.figure(\n",
    "    frame_height=250, frame_width=350, x_axis_label=\"T (K)\", y_axis_label=\"k (1/s)\"\n",
    ")\n",
    "p.line(Tstar, kstar, line_width=2, color=\"orange\")\n",
    "p = bebi103.viz.fill_between(\n",
    "    Tstar,\n",
    "    high,\n",
    "    Tstar,\n",
    "    low,\n",
    "    show_line=False,\n",
    "    patch_kwargs=dict(color=\"orange\", alpha=0.3),\n",
    "    p=p,\n",
    ")\n",
    "p.circle(source=df, x=\"T (K)\", y=\"k (1/s)\")\n",
    "\n",
    "bokeh.io.show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is important to remember that the line in the plot is the most probable function *for the given choice of hyperparameters and* $\\sigma$. The envelope is *not* posterior predictive of data sets. Rather in encompasses the exent of 95% of the nonparametric functions $f(T)$. If you wanted a posterior predictive plot, you would have to sample out of the posterior to generate *data*.\n",
    "\n",
    "Nonetheless, this plot is clear enough to show that this fit does not quite hold muster. It tends to dampen out the variation in the data in favor of a flatter curve. Furthermore, the credible region seems unreasonably wide for these data. Maybe this was not the best choice of hyperparameters. In the next lesson, we will learn how to better choose, or more specifically sample, hyperparameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "\n",
    "In this lesson, we have introduced Gaussian processes and demonstrated the useful simplifications when a GP prior is used with a Normal likelihood. Even for these convenient models, we are still left to infer the hyperparameters. Inference for GP priors with non-Normal likelihoods adds extra challenges because we do not have the benefit of conjugacy and cannot marginalize away the function values. We will address these practical issues in the next lesson."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "bebi103.stan.clean_cmdstan()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.11.5\n",
      "IPython version      : 8.15.0\n",
      "\n",
      "numpy     : 1.26.2\n",
      "pandas    : 2.1.4\n",
      "cmdstanpy : 1.2.0\n",
      "arviz     : 0.17.0\n",
      "bokeh     : 3.3.0\n",
      "bebi103   : 0.1.20\n",
      "jupyterlab: 4.0.10\n",
      "\n",
      "cmdstan   : 2.34.0\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -v -p numpy,pandas,cmdstanpy,arviz,bokeh,bebi103,jupyterlab\n",
    "print(\"cmdstan   :\", bebi103.stan.cmdstan_version())"
   ]
  }
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