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<section id="examples">
<h1>Examples<a class="headerlink" href="#examples" title="Link to this heading">¶</a></h1>
<p>A curated set of <a class="reference external" href="https://github.com/pgmpy/pgmpy/tree/dev/examples">Jupyter notebooks</a> that demonstrate the most common tasks in pgmpy - building models, learning from data, inference, and causal analysis.</p>
<section id="defining-bayesian-networks">
<h2>Defining Bayesian Networks<a class="headerlink" href="#defining-bayesian-networks" title="Link to this heading">¶</a></h2>
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<li class="toctree-l1"><a class="reference internal" href="examples/Creating_Discrete_BN.html">Creating Discrete Bayesian Networks</a></li>
<li class="toctree-l1"><a class="reference internal" href="examples/Creating_Linear_BN.html">Creating Linear Gaussian Bayesian Networks</a></li>
<li class="toctree-l1"><a class="reference internal" href="examples/Defining_CPDs.html">How to define TabularCPD and LinearGaussianCPD</a></li>
<li class="toctree-l1"><a class="reference internal" href="examples/Basic_Operations_on_BN.html">Basic Operations on Bayesian Networks</a></li>
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<h2>Causal Discovery / Structure Learning<a class="headerlink" href="#causal-discovery-structure-learning" title="Link to this heading">¶</a></h2>
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<li class="toctree-l1"><a class="reference internal" href="examples/Structure_Learning.html">Structure Learning in Bayesian Networks</a></li>
<li class="toctree-l1"><a class="reference internal" href="examples/Structure_Learning_Chow_Liu.html">Learning Tree Structure from Data using the Chow-Liu Algorithm</a></li>
<li class="toctree-l1"><a class="reference internal" href="examples/Structure_Learning_TAN.html">Learning Tree-augmented Naive Bayes (TAN) Structure from Data</a></li>
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<section id="parameter-estimation">
<h2>Parameter Estimation<a class="headerlink" href="#parameter-estimation" title="Link to this heading">¶</a></h2>
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<li class="toctree-l1"><a class="reference internal" href="examples/Parameter_Learning_Discrete_BN.html">Parameter Learning in Discrete Bayesian Networks</a></li>
<li class="toctree-l1"><a class="reference internal" href="examples/Parameter_Learning_Factor_Graphs.html">Marginal Learning in Discrete Markov Networks</a></li>
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<h2>Probabilistic Inference<a class="headerlink" href="#probabilistic-inference" title="Link to this heading">¶</a></h2>
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<li class="toctree-l1"><a class="reference internal" href="examples/Inference_Discrete_BN.html">Inference in Discrete Bayesian Network</a></li>
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<h2>Causal Inference<a class="headerlink" href="#causal-inference" title="Link to this heading">¶</a></h2>
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<li class="toctree-l1"><a class="reference internal" href="examples/Causal_Inference.html">Causal Inference Examples</a></li>
<li class="toctree-l1"><a class="reference internal" href="examples/Causal_Games.html">Causal Games</a></li>
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<section id="simulations">
<h2>Simulations<a class="headerlink" href="#simulations" title="Link to this heading">¶</a></h2>
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<li class="toctree-l1"><a class="reference internal" href="examples/Simulating_Data.html">Simulating Data From Bayesian Networks</a></li>
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<section id="extending-pgmpy">
<h2>Extending pgmpy<a class="headerlink" href="#extending-pgmpy" title="Link to this heading">¶</a></h2>
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<li class="toctree-l1"><a class="reference internal" href="examples/Extending_pgmpy.html">Extending pgmpy</a></li>
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