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<!doctype html>
<html>
<head>
<meta http-equiv="Content-Type" content="text/html;charset=utf-8">
<title>Bayesian Model Evaluation and Criticism</title>
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<table style="font-size: 150%;">
<tr>
<td colspan="2" style="font-size: 300%">Bayesian Model Evaluation and Criticism</td>
</tr>
<tr>
<td><em>Ravin Kumar</em></td>
<td align="right"><em>Colin Carroll</em></td>
</tr>
<tr>
<td><a href="http://canyon289.github.io/">canyon289.github.io</a></td>
<td align="right"><a href="https://colindcarroll.com/">colindcarroll.com</a></td>
</tr>
<tr>
<td><a href="https://twitter.com/canyon289">@canyon289</a></td>
<td align="right"><a href="https://twitter.com/colindcarroll">@colindcarroll</a></td>
</tr>
<tr>
<td colspan="2" id='graph' align="center"> </td>
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</table>
<p>
Good statisticians are able to explain their choices, justify their numbers,
evaluate their own models, and share their results in a reproducible fashion!
This tutorial demonstrates how to do the above using ArviZ.
</p>
<h3>Contents:</h3>
<ol>
<li><a href="#Prereading">Optional Prereading</a> </li>
<li><a href="#Slides">Slides</a></li>
<li><a href="#Installation">Installation</a></li>
<li><a href="#Acknowledgements">Acknowledgements</a></li>
<li><a href="#Feedback">Feedback</a></li>
</ol>
<h2 id="Prereading">Optional Prereading</h2>
<p>
No prior work is required for this tutorial but if you'd like to prepare
we've provided
<a href="https://github.com/canyon289/bayesian-model-evaluation/blob/master/HelpfulResources.md
">a list of resources </a>
<h2 id="Slides">Slides</h2>
The material is all covered in notebooks, but slides motivating MCMC and introducing trace plots are available
<a href="lessonplans/mcmc_basics/">here</a>.
<h2 id="Installation">Installation</h2>
<ol>
<li><em>Clone the repository locally</em>
<p>In your terminal, use <code>git</code> to clone the repository locally:</p>
<pre class="code">
git clone git@github.com:arviz-devs/bayesian-model-evaluation.git
</pre>
<p>Alternatively, you can download the zip file of the repository at the top of the main page of the
repository. If you prefer not to use git or don't have experience with it, this a good option.</p>
</li>
<li><em>Download Anaconda (if you haven't already)</em>
<p>If you do not already have the <a href="https://www.anaconda.com/download/">Anaconda Distribution</a>
of
Python 3, go get it (note: you can also set up your project environment w/out Anaconda using
<code>pip</code> to install the required packages; however Anaconda is great for data science and we
encourage you to use it).</p>
</li>
<li><em>Set up your environment</em>
<ol type="a">
<li><em><code>conda</code> users</em>
<p>If this is the first time you are setting up your compute environment, use <code>conda</code>
to
<em>create an environment</em>:</p>
<pre class="code">conda env create -n bayes-eval</pre>
<p>To <em>activate the environment</em>, use the <code>conda activate</code> command:</p>
<pre class="code">conda activate bayes-eval</pre>
<p><em>If you get an error activating the environment</em>, try to use the older <code>source
activate</code> command:</p>
<pre class="code">source activate bayes-eval</pre>
<p>Then follow the instructions for <code>pip</code> users.</p>
</li>
<li><em><code>pip</code> users</em>
<p>Install all of the packages listed in the <code>requirements.txt</code> file:</p>
<pre class="code">pip install -r requirements.txt</pre>
</li>
<li><em><code>Docker</code> users</em>
<p>An image can be built from the root directory of the repository using the following command.
This
will build an image on your computer with all dependencies and environment installed:</p>
<pre class="code">./scripts/container.sh --build</pre>
<p>Once an image is built a container can be started with the command:</p>
<pre class="code">./scripts/container.sh --notebook</pre>
<p>In your terminal a URL for the notebook server will be displayed. Copy and paste that into a
browser. With that you'll have Jupyter in a container! If you're using docker you can skip
step
4.</p>
</li>
<li><em>Do not want to mess with dev ops</em>
<p>You can click <a
href="https://mybinder.org/v2/gh/arviz-devs/bayesian-model-evaluation/master"><img
src="https://mybinder.org/badge.svg" /></a> to get a Binder session on which you can
compute and code.</p>
</li>
</ol>
<li><em>Open Jupyter Lab!</em>
<p>In the terminal, navigate to this directory and execute <code>jupyter lab</code></p>
<p>Navigate to the <code>notebooks</code> directory, then <code>1_BayesianWorkflow</code>, and open the
notebook <code>1_Ins_BayesRefresher.ipynb</code>.</p>
<ol type="a">
<li><em>Want to view static notebooks</em>
<p>If you are only interested in viewing the static versions of the notebooks, you can <a
href="https://github.com/arviz-devs/bayesian-model-evaluation/tree/master/notebooks">view
them on github</a>.</p>
</li>
</ol>
</li>
</ol>
<h2 id="Acknowledgements">Acknowledgements</h2>
<p>We would like to thank the whole Bayes community for being open with learnings and material. For this
tutorial in
particular we'd like to thank Ari Hartikainen, Osvaldo Martin, and Eric Ma for providing feedback and
content.
</p>
<h2 id="Feedback">Feedback</h2>
<p>We have a Google form we would love you to use <a href="https://forms.gle/cUStHUo5k9yZcrUn9">here</a>. We'll
use
this information to help improve the teaching and delivery of the material. <a
href="https://github.com/arviz-devs/bayesian-model-evaluation/issues">Issues</a> and <a
href="https://github.com/arviz-devs/bayesian-model-evaluation/pulls">pull requests</a> are also welcomed
and
encouraged if you would like!
</p>
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