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<!DOCTYPE html>
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<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
<title>Supported Data Types — pgmpy 0.1.23 documentation</title>
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<p>pgmpy is a pure python implementation for Bayesian Networks with a focus on
modularity and extensibility. Implementations of various alogrithms for Structure
Learning, Parameter Estimation, Approximate (Sampling Based) and Exact
inference, and Causal Inference are available.</p>
<section id="supported-data-types">
<h1>Supported Data Types<a class="headerlink" href="#supported-data-types" title="Permalink to this heading">¶</a></h1>
<table class="docutils align-default">
<thead>
<tr class="row-odd"><th class="head"></th>
<th class="head"><p>Structure Learning</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>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><strong>Discrete</strong></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 (only PC)</p></td>
<td><p>No</p></td>
<td><p>Yes (partial)</p></td>
<td><p>No</p></td>
</tr>
<tr class="row-even"><td><p><strong>Hybrid</strong></p></td>
<td><p>No</p></td>
<td><p>No</p></td>
<td><p>No</p></td>
<td><p>No</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>
</tr>
</tbody>
</table>
</section>
<section id="algorithms">
<h1>Algorithms<a class="headerlink" href="#algorithms" title="Permalink to this heading">¶</a></h1>
<table class="docutils align-default">
<thead>
<tr class="row-odd"><th class="head"><p>Structure Learning</p></th>
<th class="head"><p>Parameter Learning</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>Hill-Climb Search</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>Tree Search</p></td>
<td><p>Expectation Maximization</p></td>
<td><p>MPLP</p></td>
<td></td>
</tr>
<tr class="row-odd"><td><p>Max-Min Hill-Climb</p></td>
<td></td>
<td><p>Sampling methods</p></td>
<td></td>
</tr>
<tr class="row-even"><td><p>Exhaustive Search</p></td>
<td></td>
<td></td>
<td></td>
</tr>
</tbody>
</table>
<p>Example notebooks are also available at: <a class="reference external" href="https://github.com/pgmpy/pgmpy/tree/dev/examples">https://github.com/pgmpy/pgmpy/tree/dev/examples</a></p>
<p>Tutorial notebooks are also available at: <a class="reference external" href="https://github.com/pgmpy/pgmpy_notebook">https://github.com/pgmpy/pgmpy_notebook</a></p>
</section>
<section id="citation">
<h1>Citation<a class="headerlink" href="#citation" title="Permalink 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>Ankan, Ankur, Abinash, Panda. "pgmpy: Probabilistic Graphical Models using Python." Proceedings of the Python in Science Conference. SciPy, 2015.
</pre></div>
</div>
<p>Bibtex:</p>
<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>@inproceedings{Ankan2015,
series = {SciPy},
title = {pgmpy: Probabilistic Graphical Models using Python},
ISSN = {2575-9752},
url = {http://dx.doi.org/10.25080/Majora-7b98e3ed-001},
DOI = {10.25080/majora-7b98e3ed-001},
booktitle = {Proceedings of the Python in Science Conference},
publisher = {SciPy},
author = {Ankan, Ankur and Panda, Abinash},
year = {2015},
collection = {SciPy}
}
</pre></div>
</div>
</section>
<section id="indices-and-tables">
<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink 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>
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