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<!DOCTYPE html>
<html lang="en" data-content_root="./">
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
<title>Supported Data Types — pgmpy 0.1.23 documentation</title>
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<a class="reference external image-reference" href="https://github.com/pgmpy/pgmpy/actions?query=branch%3Adev"><img alt="https://github.com/pgmpy/pgmpy/actions/workflows/ci.yml/badge.svg?branch=dev" src="https://github.com/pgmpy/pgmpy/actions/workflows/ci.yml/badge.svg?branch=dev" /></a>
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</div>
<p>pgmpy is a Python package for causal inference and probabilistic inference
using Directed Acyclic Graphs (DAGs) and Bayesian Networks with a focus on
modularity and extensibility. Implementations of various algorithms for Causal
Discovery (a.k.a, Structure Learning), Parameter Estimation, Approximate
(Sampling Based) and Exact inference, and Causal Inference are available.</p>
<div class="line-block">
<div class="line"><br /></div>
</div>
<figure class="align-default" id="id1">
<img alt="_images/pgmpy_workflow.png" src="_images/pgmpy_workflow.png" />
<figcaption>
<p><span class="caption-text">Possible Workflows in pgmpy for Directed Acyclic Graphs (DAGs) and Bayesian Networks (BNs).</span><a class="headerlink" href="#id1" title="Link to this image">¶</a></p>
</figcaption>
</figure>
<div class="line-block">
<div class="line"><br /></div>
</div>
<section id="supported-data-types">
<h1>Supported Data Types<a class="headerlink" href="#supported-data-types" title="Link to this heading">¶</a></h1>
<table class="docutils align-default">
<thead>
<tr class="row-odd"><th class="head"></th>
<th class="head"><p>Causal Discovery</p></th>
<th class="head"><p>Parameter Estimation</p></th>
<th class="head"><p>Causal Inference</p></th>
<th class="head"><p>Probabilistic Inference</p></th>
<th class="head"><p>Simulations</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><strong>Categorical</strong></p></td>
<td><p>Yes</p></td>
<td><p>Yes</p></td>
<td><p>Yes</p></td>
<td><p>Yes</p></td>
<td><p>Yes</p></td>
</tr>
<tr class="row-odd"><td><p><strong>Continuous</strong></p></td>
<td><p>Yes</p></td>
<td><p>Yes</p></td>
<td><p>Yes (partial)</p></td>
<td><p>Yes</p></td>
<td><p>Yes</p></td>
</tr>
<tr class="row-even"><td><p><strong>Mixed</strong></p></td>
<td><p>Yes</p></td>
<td><p>No</p></td>
<td><p>No</p></td>
<td><p>No</p></td>
<td><p>Yes</p></td>
</tr>
<tr class="row-odd"><td><p><strong>Time Series</strong></p></td>
<td><p>No</p></td>
<td><p>Yes</p></td>
<td><p>Yes (ApproximateInference)</p></td>
<td><p>Yes</p></td>
<td><p>Yes</p></td>
</tr>
</tbody>
</table>
<div class="line-block">
<div class="line"><br /></div>
</div>
</section>
<section id="algorithms">
<h1>Algorithms<a class="headerlink" href="#algorithms" title="Link to this heading">¶</a></h1>
<table class="docutils align-default">
<thead>
<tr class="row-odd"><th class="head"><p>Causal Discovery / Structure Learning</p></th>
<th class="head"><p>Parameter Estimation</p></th>
<th class="head"><p>Probabilistic Inference</p></th>
<th class="head"><p>Causal Inference</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>PC with variants</p></td>
<td><p>Maximum Likelihood</p></td>
<td><p>Variable Elimination</p></td>
<td><p>do-operation</p></td>
</tr>
<tr class="row-odd"><td><p>Greedy Equivalence Search(GES)</p></td>
<td><p>Bayesian Estimator</p></td>
<td><p>Belief Propagation</p></td>
<td><p>adjustment sets</p></td>
</tr>
<tr class="row-even"><td><p>Hill-Climb Search</p></td>
<td><p>Expectation Maximization (EM)</p></td>
<td><p>MPLP</p></td>
<td></td>
</tr>
<tr class="row-odd"><td><p>Expert In The Loop</p></td>
<td></td>
<td><p>Sampling methods</p></td>
<td></td>
</tr>
<tr class="row-even"><td><p>Tree Search</p></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="row-odd"><td><p>Max-Min Hill-Climb</p></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="row-even"><td><p>Exhaustive Search</p></td>
<td></td>
<td></td>
<td></td>
</tr>
</tbody>
</table>
<div class="line-block">
<div class="line"><br /></div>
</div>
</section>
<section id="examples">
<h1>Examples<a class="headerlink" href="#examples" title="Link to this heading">¶</a></h1>
<p><strong>Example notebooks:</strong> <a class="reference external" href="https://pgmpy.org/examples.html">https://pgmpy.org/examples.html</a></p>
<p><strong>Tutorial notebooks:</strong> <a class="reference external" href="https://pgmpy.org/tutorial.html">https://pgmpy.org/tutorial.html</a></p>
<div class="line-block">
<div class="line"><br /></div>
</div>
</section>
<section id="citation">
<h1>Citation<a class="headerlink" href="#citation" title="Link to this heading">¶</a></h1>
<p>If you use pgmpy in your scientific work, please consider citing us:</p>
<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>Ankur Ankan, & Johannes Textor (2024). pgmpy: A Python Toolkit for Bayesian Networks. Journal of Machine Learning Research, 25(265), 1–8.
</pre></div>
</div>
<p>Bibtex:</p>
<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>@article{Ankan2024,
author = {Ankur Ankan and Johannes Textor},
title = {pgmpy: A Python Toolkit for Bayesian Networks},
journal = {Journal of Machine Learning Research},
year = {2024},
volume = {25},
number = {265},
pages = {1--8},
url = {http://jmlr.org/papers/v25/23-0487.html}
}
</pre></div>
</div>
</section>
<section id="indices-and-tables">
<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Link to this heading">¶</a></h1>
<ul class="simple">
<li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></p></li>
<li><p><a class="reference internal" href="py-modindex.html"><span class="std std-ref">Module Index</span></a></p></li>
<li><p><a class="reference internal" href="search.html"><span class="std std-ref">Search Page</span></a></p></li>
</ul>
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