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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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<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>
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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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<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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<li class="toctree-l1"><a class="reference internal" href="examples/Functional_Bayesian_Network_Tutorial.html">Functional Bayesian Networks</a></li>
<li class="toctree-l1"><a class="reference internal" href="examples/Junction_Tree_Inference.html">Junction Tree Exact Inference</a></li>
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<section id="example-notebooks">
<h1>Example Notebooks<a class="headerlink" href="#example-notebooks" 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, running inference, and performing causal analysis. Use this page to find a
notebook first, then jump to the matching guide or API section for details.</p>
<section id="defining-bayesian-networks">
<h2>Defining Bayesian Networks<a class="headerlink" href="#defining-bayesian-networks" title="Link to this heading">#</a></h2>
<p>See the <a class="reference internal" href="guides/custom_model.html"><span class="doc">Defining a Custom Model</span></a> guide for background.</p>
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Creating Discrete BN</div>
<p class="sd-card-text">Build a discrete Bayesian Network from scratch.</p>
</div>
<a class="sd-stretched-link sd-hide-link-text reference internal" href="examples/Creating_Discrete_BN.html"><span class="doc">Creating Discrete Bayesian Networks</span></a></div>
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Creating Linear BN</div>
<p class="sd-card-text">Define a linear Gaussian Bayesian Network.</p>
</div>
<a class="sd-stretched-link sd-hide-link-text reference internal" href="examples/Creating_Linear_BN.html"><span class="doc">Creating Linear Gaussian Bayesian Networks</span></a></div>
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Dynamic BN</div>
<p class="sd-card-text">Model temporal dependencies with a Dynamic BN.</p>
</div>
<a class="sd-stretched-link sd-hide-link-text reference internal" href="examples/Dynamic_BN.html"><span class="doc"><no title></span></a></div>
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Defining CPDs</div>
<p class="sd-card-text">Specify conditional probability distributions.</p>
</div>
<a class="sd-stretched-link sd-hide-link-text reference internal" href="examples/Defining_CPDs.html"><span class="doc">How to define TabularCPD and LinearGaussianCPD</span></a></div>
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Basic Operations on BN</div>
<p class="sd-card-text">Inspect, modify, and validate a BN.</p>
</div>
<a class="sd-stretched-link sd-hide-link-text reference internal" href="examples/Basic_Operations_on_BN.html"><span class="doc">Basic Operations on Bayesian Networks</span></a></div>
</div>
</div>
</div>
</section>
<section id="causal-discovery-structure-learning">
<h2>Causal Discovery / Structure Learning<a class="headerlink" href="#causal-discovery-structure-learning" title="Link to this heading">#</a></h2>
<p>See the <a class="reference internal" href="guides/causal_discovery.html"><span class="doc">Causal Discovery</span></a> guide for background.</p>
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Structure Learning</div>
<p class="sd-card-text">Learn a graph structure from data.</p>
</div>
<a class="sd-stretched-link sd-hide-link-text reference internal" href="examples/Structure_Learning.html"><span class="doc">Structure Learning in Bayesian Networks</span></a></div>
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Chow-Liu Tree</div>
<p class="sd-card-text">Learn tree-structured networks efficiently.</p>
</div>
<a class="sd-stretched-link sd-hide-link-text reference internal" href="examples/Structure_Learning_Chow_Liu.html"><span class="doc">Learning Tree Structure from Data using the Chow-Liu Algorithm</span></a></div>
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TAN</div>
<p class="sd-card-text">Learn a tree-augmented Naive Bayes model.</p>
</div>
<a class="sd-stretched-link sd-hide-link-text reference internal" href="examples/Structure_Learning_TAN.html"><span class="doc">Learning Tree-augmented Naive Bayes (TAN) Structure from Data</span></a></div>
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Expert Knowledge</div>
<p class="sd-card-text">Incorporate domain constraints into learning.</p>
</div>
<a class="sd-stretched-link sd-hide-link-text reference internal" href="examples/Expert_Knowledge.html"><span class="doc">Expert Knowledge Integration with Causal Discovery</span></a></div>
</div>
</div>
</div>
</section>
<section id="parameter-estimation">
<h2>Parameter Estimation<a class="headerlink" href="#parameter-estimation" title="Link to this heading">#</a></h2>
<p>See the <a class="reference internal" href="guides/parameter_estimation.html"><span class="doc">Parameter Estimation</span></a> guide for background.</p>
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Discrete BN Parameters</div>
<p class="sd-card-text">Fit CPDs for a discrete BN.</p>
</div>
<a class="sd-stretched-link sd-hide-link-text reference internal" href="examples/Parameter_Learning_Discrete_BN.html"><span class="doc">Parameter Learning in Discrete Bayesian Networks</span></a></div>
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Factor Graph Parameters</div>
<p class="sd-card-text">Estimate parameters for factor graphs.</p>
</div>
<a class="sd-stretched-link sd-hide-link-text reference internal" href="examples/Parameter_Learning_Factor_Graphs.html"><span class="doc">Marginal Learning in Discrete Markov Networks</span></a></div>
</div>
</div>
</div>
</section>
<section id="probabilistic-inference">
<h2>Probabilistic Inference<a class="headerlink" href="#probabilistic-inference" title="Link to this heading">#</a></h2>
<p>See the <a class="reference internal" href="guides/probabilistic_inference.html"><span class="doc">Probabilistic Inference</span></a> guide for background.</p>
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Inference in Discrete BN</div>
<p class="sd-card-text">Query posterior probabilities with evidence.</p>
</div>
<a class="sd-stretched-link sd-hide-link-text reference internal" href="examples/Inference_Discrete_BN.html"><span class="doc">Inference in Discrete Bayesian Network</span></a></div>
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Monty Hall</div>
<p class="sd-card-text">Solve the Monty Hall problem with a BN.</p>
</div>
<a class="sd-stretched-link sd-hide-link-text reference internal" href="examples/Monty_Hall.html"><span class="doc">Monty Hall Problem</span></a></div>
</div>
</div>
</div>
</section>
<section id="causal-inference">
<h2>Causal Inference<a class="headerlink" href="#causal-inference" title="Link to this heading">#</a></h2>
<p>See the <a class="reference internal" href="guides/causal_identification.html"><span class="doc">Causal Identification</span></a> and <a class="reference internal" href="guides/causal_estimation.html"><span class="doc">Causal Estimation</span></a> guides for background.</p>
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Causal Inference</div>
<p class="sd-card-text">Estimate causal effects from data.</p>
</div>
<a class="sd-stretched-link sd-hide-link-text reference internal" href="examples/Causal_Inference.html"><span class="doc">Causal Inference Examples</span></a></div>
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Causal Games</div>
<p class="sd-card-text">Explore causal reasoning via games.</p>
</div>
<a class="sd-stretched-link sd-hide-link-text reference internal" href="examples/Causal_Games.html"><span class="doc">Causal Games</span></a></div>
</div>
</div>
</div>
</section>
<section id="simulations">
<h2>Simulations<a class="headerlink" href="#simulations" title="Link to this heading">#</a></h2>
<p>See the <a class="reference internal" href="guides/simulations.html"><span class="doc">Simulations</span></a> guide for background.</p>
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Simulating Data</div>
<p class="sd-card-text">Generate synthetic samples from a BN.</p>
</div>
<a class="sd-stretched-link sd-hide-link-text reference internal" href="examples/Simulating_Data.html"><span class="doc">Simulating Data From Bayesian Networks</span></a></div>
</div>
</div>
</div>
</section>
<section id="extending-pgmpy">
<h2>Extending pgmpy<a class="headerlink" href="#extending-pgmpy" title="Link to this heading">#</a></h2>
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Extending pgmpy</div>
<p class="sd-card-text">Add custom models, estimators, or utilities.</p>
</div>
<a class="sd-stretched-link sd-hide-link-text reference internal" href="examples/Extending_pgmpy.html"><span class="doc">Extending pgmpy</span></a></div>
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Functional Bayesian Network</div>
<p class="sd-card-text">Build functional CPDs for hybrid models.</p>
</div>
<a class="sd-stretched-link sd-hide-link-text reference internal" href="examples/Functional_Bayesian_Network_Tutorial.html"><span class="doc">Functional Bayesian Networks</span></a></div>
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Junction Tree Inference</div>
<p class="sd-card-text">Perform inference using junction trees.</p>
</div>
<a class="sd-stretched-link sd-hide-link-text reference internal" href="examples/Junction_Tree_Inference.html"><span class="doc">Junction Tree Exact Inference</span></a></div>
</div>
</div>
</div>
</section>
<section id="tutorial-notebooks">
<h2>Tutorial Notebooks<a class="headerlink" href="#tutorial-notebooks" title="Link to this heading">#</a></h2>
<p>A series of in-depth tutorial notebooks that walk through pgmpy’s core concepts step by step — from
probabilistic graphical model basics to real-world applications.</p>
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1. Introduction to PGMs</div>
<p class="sd-card-text">Foundations of probabilistic graphical models.</p>
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<span class="sd-stretched-link sd-hide-link-text">detailed_notebooks/1.IntroductiontoProbabilisticGraphicalModels</span></div>
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2. Bayesian Networks</div>
<p class="sd-card-text">Build and reason with Bayesian Networks.</p>
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<span class="sd-stretched-link sd-hide-link-text">detailed_notebooks/2.BayesianNetworks</span></div>
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3. Causal Bayesian Networks</div>
<p class="sd-card-text">Causal reasoning with Bayesian Networks.</p>
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<span class="sd-stretched-link sd-hide-link-text">detailed_notebooks/3.CausalBayesianNetworks</span></div>
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4. Markov Models</div>
<p class="sd-card-text">Undirected graphical models and Markov Networks.</p>