{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true, "scrolled": true, "tags": [ "hide_input" ] }, "outputs": [], "source": [ "from IPython.display import HTML\n", "from IPython.display import display\n", "\n", "display(HTML(\"\"))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true, "tags": [ "hide_input" ] }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np, scipy, scipy.stats as stats, pandas as pd, matplotlib.pyplot as plt, seaborn as sns\n", "import statsmodels, statsmodels.api as sm\n", "import sympy, sympy.stats\n", "import pymc3 as pm\n", "import daft\n", "import xarray, numba, arviz as az\n", "\n", "pd.set_option('display.max_columns', 500)\n", "pd.set_option('display.width', 1000)\n", "# pd.set_option('display.float_format', lambda x: '%.2f' % x)\n", "np.set_printoptions(edgeitems=10)\n", "np.set_printoptions(linewidth=1000)\n", "np.set_printoptions(suppress=True)\n", "np.core.arrayprint._line_width = 180\n", "\n", "SEED = 42\n", "np.random.seed(SEED)\n", "\n", "sns.set()\n", "\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This blog post is part of the [Series: Monte Carlo Methods](https://weisser-zwerg.dev/posts/series-monte-carlo-methods/).\n", "\n", "You can find this blog post on [weisser-zwerg.dev](https://weisser-zwerg.dev/posts/monte-carlo-markov-chain-monte-carlo/) or on [github](https://github.com/cs224/blog-series-monte-carlo-methods) as either [html](https://rawcdn.githack.com/cs224/blog-series-monte-carlo-methods/main/0020-markov-chain-monte-carlo.html) or via [nbviewer](https://nbviewer.jupyter.org/github/cs224/blog-series-monte-carlo-methods/blob/main/0020-markov-chain-monte-carlo.ipynb?flush_cache=true)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Markov chain Monte Carlo (MCMC)" ] }, { "cell_type": "markdown", "metadata": { "toc": true }, "source": [ "