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<h1 align="center">
<code>awesome-bayes-nets</code>
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<p align="center">
<a href="https://awesome.re"><img src="data:image/svg+xml;base64,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" /></a>
</p>
<p align="center">
<sub>#bayesrocks</sub>
</p>
<p><strong>awesome-bayes-nets</strong> is a curated and structured list of <em>Books</em>, <em>Research Papers</em>, and <em>Software</em> for <strong>Bayesian Networks</strong> (BNs).</p>
<p>Papers are sorted by year and topics. This was inspired (and modeled on) Antonio Vergari's <a href="https://github.com/arranger1044/awesome-spn"><code>awesome-spn</code></a> repository, which in turn was inspired by the <a href="http://spn.cs.washington.edu/">SPN page</a> at the University of Washington. Some inspiration was also drawn from the original <a href="http://www.cs.huji.ac.il/~galel/Repository/">Bayesian Network Repository</a> by Gal Elidan and Nir Friedman.</p>
<h2 id="contributing">Contributing</h2>
<p>We have adopted the <a href=".github/CODE_OF_CONDUCT.md"><em>Contributor Code of Covenant</em></a>. Contributions are appreciated, but please read the <a href="CONTRIBUTING.md"><code>CONTRIBUTING.md</code></a> and follow the guidelines provided for issues and pull requests.</p>
<p><a href="https://hayesall.com/">Alexander L. Hayes</a> currently maintains this list. He is notified when new <a href="https://github.com/batflyer/awesome-bayes-nets/issues">issues</a> or <a href="https://github.com/batflyer/awesome-bayes-nets/pulls">pull requests</a> are submitted, but may not always respond immediately. He can also be reached at <a href="mailto:hayesall@iu.edu"><code>hayesall@iu.edu</code></a>.</p>
<hr />
<h2 id="contents">Contents</h2>
<p><em>Do we need a New Topic?</em> See <a href="CONTRIBUTING.md#new-topics">here</a>.</p>
<ol type="1">
<li><a href="#papers-by-year">Papers by Year</a>
<ul>
<li><a href="#2018">2018</a></li>
<li><a href="#2017">2017</a></li>
<li><a href="#2016">2016</a></li>
<li><a href="#2015">2015</a></li>
<li><a href="#2010">2010</a></li>
<li><a href="#2002">2002</a></li>
<li><a href="#2000">2000</a></li>
<li><a href="#1999">1999</a></li>
<li><a href="#1998">1998</a></li>
<li><a href="#1997">1997</a></li>
<li><a href="#1996">1996</a></li>
<li><a href="#1995">1995</a></li>
<li><a href="#1994">1994</a></li>
<li><a href="#1993">1993</a></li>
<li><a href="#1992">1992</a></li>
<li><a href="#1979">1979</a></li>
<li><a href="#1968">1968</a></li>
</ul></li>
<li><a href="#papers-by-topic">Papers by Topic</a>
<ul>
<li><a href="#structure-learning">structure-learning</a></li>
<li><a href="#structure-and-parameter-learning">structure-and-parameter-learning</a></li>
<li><a href="#applications">applications</a></li>
<li><a href="#theory">theory</a></li>
</ul></li>
<li><a href="#resources">Resources</a></li>
<li><a href="#further-reading">Further Reading</a></li>
</ol>
<h2 id="papers-by-year">Papers by Year</h2>
<h3 id="section">2018</h3>
<ul>
<li>Jacob Schreiber. (2018). &quot;<a href="http://jmlr.org/papers/v18/17-636.html">pomegranate: Fast and Flexible Probabilistic Modeling in Python</a>.&quot; Journal of Machine Learning Research. <a href="bib/2018/2018_schreiber.bib"><code>2018_schreiber.bib</code></a></li>
</ul>
<h3 id="section-1">2017</h3>
<ul>
<li>Schreiber, Jacob M and Noble, William S. (2017). &quot;Finding the optimal Bayesian network given a constraint graph.&quot; PeerJ Computer Science. <a href="bib/2017/2017_schreiber.bib"><code>2017_schreiber.bib</code></a></li>
</ul>
<h3 id="section-2">2016</h3>
<ul>
<li>Gorinova, Maria I. and Sarkar, Advait and Blackwell, Alan F. and Syme, Don. (2016). &quot;<a href="https://doi.org/10.1145/2858036.2858221">A Live, Multiple-Representation Probabilistic Programming Environment for Novices</a>.&quot; Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. <a href="bib/2016/2016_gorinova.bib"><code>2016_gorinova.bib</code></a></li>
</ul>
<h3 id="section-3">2015</h3>
<ul>
<li>Lowd, Daniel and Rooshenas, Amirmohammad. (2015). &quot;<a href="http://www.jmlr.org/papers/volume16/lowd15a/lowd15a.pdf">The Libra Toolkit for Probabilistic Models</a>.&quot; The Journal of Machine Learning Research. <a href="bib/2015/2015_lowd.bib"><code>2015_lowd.bib</code></a></li>
</ul>
<h3 id="section-4">2010</h3>
<ul>
<li>Gopalakrishnan, Vanathi and Lustgarten, Jonathan L. and Visweswaran, Shyam and Cooper, Gregory F.. (2010). &quot;<a href="https://doi.org/10.1093/bioinformatics/btq005">Bayesian rule learning for biomedical data mining</a>.&quot; Bioinformatics. <a href="bib/2010/2010_gopalakrishnan.bib"><code>2010_gopalakrishnan.bib</code></a></li>
</ul>
<h3 id="section-5">2002</h3>
<ul>
<li>Lerner, Uri N. (2002). &quot;<a href="https://pdfs.semanticscholar.org/5609/16ef9bf3dffee6bd74192b5987870a66fad7.pdf">Hybrid Bayesian Networks for Reasoning about Complex Systems</a>.&quot; Ph.D. Thesis. <a href="bib/2002/2002_lerner.bib"><code>2002_lerner.bib</code></a></li>
<li>Chickering, David Maxwell. (2002). &quot;<a href="http://www.jmlr.org/papers/volume2/chickering02a/chickering02a.pdf">Learning Equivalence Classes of Bayesian-Network Structures</a>.&quot; Journal of Machine Learning Research. <a href="bib/2002/2002_chickering.bib"><code>2002_chickering.bib</code></a></li>
</ul>
<h3 id="section-6">2000</h3>
<ul>
<li>Friedman, Nir and Linial, Michal and Nachman, Iftach and Pe'er, Dana. (2000). &quot;<a href="https://doi.org/10.1145/332306.332355">Using Bayesian Networks to Analyze Expression Data</a>.&quot; Proceedings of the Fourth Annual International Conference on Computational Molecular Biology. <a href="bib/2000/2000_friedman.bib"><code>2000_friedman.bib</code></a></li>
<li>Tian, Jin. (2000). &quot;<a href="https://dl.acm.org/doi/abs/10.5555/2073946.2074014">A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks</a>.&quot; Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence. <a href="bib/2000/2000_tian.bib"><code>2000_tian.bib</code></a></li>
</ul>
<h3 id="section-7">1999</h3>
<ul>
<li>Friedman, Nir and Nachman, Iftach and Peér, Dana. (1999). &quot;<a href="https://arxiv.org/abs/1301.6696">Learning Bayesian Network Structure from Massive Datasets: The &quot;Sparse Candidate&quot; Algorithm</a>.&quot; Proceedings of the Fifteenth conference on Uncertainty in Artificial Intelligence (UAI). <a href="bib/1999/1999_friedman.bib"><code>1999_friedman.bib</code></a></li>
<li>David Heckerman. (1999). &quot;A Tutorial on Learning with Bayesian Networks.&quot; Learning in Graphical Models. <a href="bib/1999/1999_heckerman.bib"><code>1999_heckerman.bib</code></a></li>
</ul>
<h3 id="section-8">1998</h3>
<ul>
<li>Ghahramani, Zoubin. (1998). &quot;<a href="https://doi.org/10.1007/BFb0053999">Learning Dynamic Bayesian Networks</a>.&quot; Adaptive Processing of Sequences and Data Structures: International Summer School on Neural Networks E.R. Caianiello Vietri sul Mare, Salerno, Italy September 6--13, 1997 Tutorial Lectures. <a href="bib/1998/1998_ghahramani.bib"><code>1998_ghahramani.bib</code></a></li>
<li>Shachter, Ross D.. (1998). &quot;<a href="https://arxiv.org/abs/1301.7412">Bayes-Ball: The Rational Pastime (for Determining Irrelevance and Requisite Information in Belief Networks and Influence Diagrams)</a>.&quot; Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI). <a href="bib/1998/1998_shachter.bib"><code>1998_shachter.bib</code></a></li>
</ul>
<h3 id="section-9">1997</h3>
<ul>
<li>Friedman, Nir and Geiger, Dan and Goldszmidt, Moises. (1997). &quot;<a href="https://doi.org/10.1023/A:1007465528199">Bayesian Network Classifiers</a>.&quot; Machine Learning. <a href="bib/1997/1997_friedman.bib"><code>1997_friedman.bib</code></a></li>
</ul>
<h3 id="section-10">1996</h3>
<ul>
<li>Chickering, David Maxwell. (1996). &quot;<a href="https://doi.org/10.1007/978-1-4612-2404-4_12">Learning Bayesian Networks is NP-Complete</a>.&quot; Learning from Data: Artificial Intelligence and Statistics V. <a href="bib/1996/1996_chickering.bib"><code>1996_chickering.bib</code></a></li>
<li>Sahami, Mehran. (1996). &quot;<a href="https://www.aaai.org/Papers/KDD/1996/KDD96-061.pdf">Learning Limited Dependence Bayesian Classifiers</a>.&quot; Knowledge Discovery and Data Mining (KDD). <a href="bib/1996/1996_sahami.bib"><code>1996_sahami.bib</code></a></li>
</ul>
<h3 id="section-11">1995</h3>
<ul>
<li>Chickering, David Maxwell. (1995). &quot;<a href="https://arxiv.org/abs/1302.4938">A Transformational Characterization of Equivalent Bayesian Network Structures</a>.&quot; Proceedings of the Eleventh conference on Uncertainty in Artificial Intelligence (UAI). <a href="bib/1995/1995_chickering.bib"><code>1995_chickering.bib</code></a></li>
<li>Heckerman, David and Geiger, Dan and Chickering, David M. (1995). &quot;<a href="https://doi.org/10.1023/A:1022623210503">Learning Bayesian Networks: The Combination of Knowledge and Statistical Data</a>.&quot; Machine Learning. <a href="bib/1995/1995_heckerman.bib"><code>1995_heckerman.bib</code></a></li>
<li>Ezawa, Kazuo J. and Schuermann, Til. (1995). &quot;<a href="https://arxiv.org/abs/1302.4945">Fraud/Uncollectible Debt Detection Using a Bayesian Network Based Learning System: A Rare Binary Outcome with Mixed Data Structures</a>.&quot; Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI). <a href="bib/1995/1995_ezawa.bib"><code>1995_ezawa.bib</code></a></li>
<li>Bouckaert, Remco Ronaldus. (1995). &quot;<a href="https://dspace.library.uu.nl/handle/1874/845">Bayesian Belief Networks: From Construction to Inference</a>.&quot; Ph.D. Thesis. <a href="bib/1995/1995_bouckaert.bib"><code>1995_bouckaert.bib</code></a></li>
</ul>
<h3 id="section-12">1994</h3>
<ul>
<li>Lam, Wai and Bacchus, Fahiem. (1994). &quot;<a href="https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-8640.1994.tb00166.x">Learning Bayesian Belief Networks: An Approach Based on the MDL Principle</a>.&quot; Computational Intelligence. <a href="bib/1994/1994_lam.bib"><code>1994_lam.bib</code></a></li>
</ul>
<h3 id="section-13">1993</h3>
<ul>
<li>Bouckaert, Remco R.. (1993). &quot;<a href="https://doi.org/10.1007/BFb0028180">Probabilistic network construction using the minimum description length principle</a>.&quot; Symbolic and Quantitative Approaches to Reasoning and Uncertainty. <a href="bib/1993/1993_bouckaert.bib"><code>1993_bouckaert.bib</code></a></li>
</ul>
<h3 id="section-14">1992</h3>
<ul>
<li>Cooper, Gregory F. and Herskovits, Edward. (1992). &quot;<a href="https://doi.org/10.1007/BF00994110">A Bayesian Method for the Induction of Probabilistic Networks from Data</a>.&quot; Machine Learning. <a href="bib/1992/1992_cooper.bib"><code>1992_cooper.bib</code></a></li>
</ul>
<h3 id="section-15">1979</h3>
<ul>
<li>Rijsbergen, C. J. Van. (1979). &quot;<a href="http://www.dcs.gla.ac.uk/Keith/Preface.html">Information Retrieval, 2nd Edition</a>.&quot; Butterworths. <a href="bib/1979/1979_rijsbergen.bib"><code>1979_rijsbergen.bib</code></a></li>
</ul>
<h3 id="section-16">1968</h3>
<ul>
<li>C. Chow and C. Liu. (1968). &quot;<a href="https://doi.org/10.1109/TIT.1968.1054142">Approximating Discrete Probability Distributions with Dependence Trees</a>.&quot; IEEE Transactions on Information Theory. <a href="bib/1968/1968_chow.bib"><code>1968_chow.bib</code></a></li>
</ul>
<h2 id="papers-by-topic">Papers by Topic</h2>
<h3 id="structure-learning">structure-learning</h3>
<ul>
<li>Bouckaert, Remco R.. (1993). &quot;<a href="https://doi.org/10.1007/BFb0028180">Probabilistic network construction using the minimum description length principle</a>.&quot; Symbolic and Quantitative Approaches to Reasoning and Uncertainty. <a href="bib/1993/1993_bouckaert.bib"><code>1993_bouckaert.bib</code></a></li>
<li>Cooper, Gregory F. and Herskovits, Edward. (1992). &quot;<a href="https://doi.org/10.1007/BF00994110">A Bayesian Method for the Induction of Probabilistic Networks from Data</a>.&quot; Machine Learning. <a href="bib/1992/1992_cooper.bib"><code>1992_cooper.bib</code></a></li>
<li>Chickering, David Maxwell. (1995). &quot;<a href="https://arxiv.org/abs/1302.4938">A Transformational Characterization of Equivalent Bayesian Network Structures</a>.&quot; Proceedings of the Eleventh conference on Uncertainty in Artificial Intelligence (UAI). <a href="bib/1995/1995_chickering.bib"><code>1995_chickering.bib</code></a></li>
<li>Heckerman, David and Geiger, Dan and Chickering, David M. (1995). &quot;<a href="https://doi.org/10.1023/A:1022623210503">Learning Bayesian Networks: The Combination of Knowledge and Statistical Data</a>.&quot; Machine Learning. <a href="bib/1995/1995_heckerman.bib"><code>1995_heckerman.bib</code></a></li>
<li>Chickering, David Maxwell. (2002). &quot;<a href="http://www.jmlr.org/papers/volume2/chickering02a/chickering02a.pdf">Learning Equivalence Classes of Bayesian-Network Structures</a>.&quot; Journal of Machine Learning Research. <a href="bib/2002/2002_chickering.bib"><code>2002_chickering.bib</code></a></li>
<li>Tian, Jin. (2000). &quot;<a href="https://dl.acm.org/doi/abs/10.5555/2073946.2074014">A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks</a>.&quot; Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence. <a href="bib/2000/2000_tian.bib"><code>2000_tian.bib</code></a></li>
<li>Sahami, Mehran. (1996). &quot;<a href="https://www.aaai.org/Papers/KDD/1996/KDD96-061.pdf">Learning Limited Dependence Bayesian Classifiers</a>.&quot; Knowledge Discovery and Data Mining (KDD). <a href="bib/1996/1996_sahami.bib"><code>1996_sahami.bib</code></a></li>
<li>Lam, Wai and Bacchus, Fahiem. (1994). &quot;<a href="https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-8640.1994.tb00166.x">Learning Bayesian Belief Networks: An Approach Based on the MDL Principle</a>.&quot; Computational Intelligence. <a href="bib/1994/1994_lam.bib"><code>1994_lam.bib</code></a></li>
<li>Friedman, Nir and Nachman, Iftach and Peér, Dana. (1999). &quot;<a href="https://arxiv.org/abs/1301.6696">Learning Bayesian Network Structure from Massive Datasets: The &quot;Sparse Candidate&quot; Algorithm</a>.&quot; Proceedings of the Fifteenth conference on Uncertainty in Artificial Intelligence (UAI). <a href="bib/1999/1999_friedman.bib"><code>1999_friedman.bib</code></a></li>
</ul>
<h3 id="structure-and-parameter-learning">structure-and-parameter-learning</h3>
<ul>
<li>Jacob Schreiber. (2018). &quot;<a href="http://jmlr.org/papers/v18/17-636.html">pomegranate: Fast and Flexible Probabilistic Modeling in Python</a>.&quot; Journal of Machine Learning Research. <a href="bib/2018/2018_schreiber.bib"><code>2018_schreiber.bib</code></a></li>
<li>Ghahramani, Zoubin. (1998). &quot;<a href="https://doi.org/10.1007/BFb0053999">Learning Dynamic Bayesian Networks</a>.&quot; Adaptive Processing of Sequences and Data Structures: International Summer School on Neural Networks E.R. Caianiello Vietri sul Mare, Salerno, Italy September 6--13, 1997 Tutorial Lectures. <a href="bib/1998/1998_ghahramani.bib"><code>1998_ghahramani.bib</code></a></li>
<li>Schreiber, Jacob M and Noble, William S. (2017). &quot;Finding the optimal Bayesian network given a constraint graph.&quot; PeerJ Computer Science. <a href="bib/2017/2017_schreiber.bib"><code>2017_schreiber.bib</code></a></li>
<li>Friedman, Nir and Geiger, Dan and Goldszmidt, Moises. (1997). &quot;<a href="https://doi.org/10.1023/A:1007465528199">Bayesian Network Classifiers</a>.&quot; Machine Learning. <a href="bib/1997/1997_friedman.bib"><code>1997_friedman.bib</code></a></li>
<li>Lowd, Daniel and Rooshenas, Amirmohammad. (2015). &quot;<a href="http://www.jmlr.org/papers/volume16/lowd15a/lowd15a.pdf">The Libra Toolkit for Probabilistic Models</a>.&quot; The Journal of Machine Learning Research. <a href="bib/2015/2015_lowd.bib"><code>2015_lowd.bib</code></a></li>
<li>David Heckerman. (1999). &quot;A Tutorial on Learning with Bayesian Networks.&quot; Learning in Graphical Models. <a href="bib/1999/1999_heckerman.bib"><code>1999_heckerman.bib</code></a></li>
</ul>
<h3 id="applications">applications</h3>
<ul>
<li>Ezawa, Kazuo J. and Schuermann, Til. (1995). &quot;<a href="https://arxiv.org/abs/1302.4945">Fraud/Uncollectible Debt Detection Using a Bayesian Network Based Learning System: A Rare Binary Outcome with Mixed Data Structures</a>.&quot; Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI). <a href="bib/1995/1995_ezawa.bib"><code>1995_ezawa.bib</code></a></li>
<li>Friedman, Nir and Linial, Michal and Nachman, Iftach and Pe'er, Dana. (2000). &quot;<a href="https://doi.org/10.1145/332306.332355">Using Bayesian Networks to Analyze Expression Data</a>.&quot; Proceedings of the Fourth Annual International Conference on Computational Molecular Biology. <a href="bib/2000/2000_friedman.bib"><code>2000_friedman.bib</code></a></li>
<li>Gopalakrishnan, Vanathi and Lustgarten, Jonathan L. and Visweswaran, Shyam and Cooper, Gregory F.. (2010). &quot;<a href="https://doi.org/10.1093/bioinformatics/btq005">Bayesian rule learning for biomedical data mining</a>.&quot; Bioinformatics. <a href="bib/2010/2010_gopalakrishnan.bib"><code>2010_gopalakrishnan.bib</code></a></li>
<li>Gorinova, Maria I. and Sarkar, Advait and Blackwell, Alan F. and Syme, Don. (2016). &quot;<a href="https://doi.org/10.1145/2858036.2858221">A Live, Multiple-Representation Probabilistic Programming Environment for Novices</a>.&quot; Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. <a href="bib/2016/2016_gorinova.bib"><code>2016_gorinova.bib</code></a></li>
</ul>
<h3 id="theory">theory</h3>
<ul>
<li>Bouckaert, Remco Ronaldus. (1995). &quot;<a href="https://dspace.library.uu.nl/handle/1874/845">Bayesian Belief Networks: From Construction to Inference</a>.&quot; Ph.D. Thesis. <a href="bib/1995/1995_bouckaert.bib"><code>1995_bouckaert.bib</code></a></li>
<li>Shachter, Ross D.. (1998). &quot;<a href="https://arxiv.org/abs/1301.7412">Bayes-Ball: The Rational Pastime (for Determining Irrelevance and Requisite Information in Belief Networks and Influence Diagrams)</a>.&quot; Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI). <a href="bib/1998/1998_shachter.bib"><code>1998_shachter.bib</code></a></li>
<li>C. Chow and C. Liu. (1968). &quot;<a href="https://doi.org/10.1109/TIT.1968.1054142">Approximating Discrete Probability Distributions with Dependence Trees</a>.&quot; IEEE Transactions on Information Theory. <a href="bib/1968/1968_chow.bib"><code>1968_chow.bib</code></a></li>
<li>Lerner, Uri N. (2002). &quot;<a href="https://pdfs.semanticscholar.org/5609/16ef9bf3dffee6bd74192b5987870a66fad7.pdf">Hybrid Bayesian Networks for Reasoning about Complex Systems</a>.&quot; Ph.D. Thesis. <a href="bib/2002/2002_lerner.bib"><code>2002_lerner.bib</code></a></li>
<li>Chickering, David Maxwell. (1996). &quot;<a href="https://doi.org/10.1007/978-1-4612-2404-4_12">Learning Bayesian Networks is NP-Complete</a>.&quot; Learning from Data: Artificial Intelligence and Statistics V. <a href="bib/1996/1996_chickering.bib"><code>1996_chickering.bib</code></a></li>
<li>Rijsbergen, C. J. Van. (1979). &quot;<a href="http://www.dcs.gla.ac.uk/Keith/Preface.html">Information Retrieval, 2nd Edition</a>.&quot; Butterworths. <a href="bib/1979/1979_rijsbergen.bib"><code>1979_rijsbergen.bib</code></a></li>
</ul>
<h2 id="resources">Resources</h2>
<p><strong>Blog Posts and Short Overviews</strong></p>
<ul>
<li><a href="https://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html">&quot;A Brief Introduction to Graphical Models and Bayesian Networks,&quot; Kevin Murphy</a></li>
<li><a href="https://personal.utdallas.edu/~nrr150130/gmbook/bayes.html">&quot;Directed Graphical Models,&quot; Nicholas Ruozzi</a></li>
<li><a href="https://ermongroup.github.io/cs228-notes/representation/directed/">&quot;Bayesian networks,&quot; Stefano Ermon</a></li>
<li><a href="https://towardsdatascience.com/introduction-to-bayesian-networks-81031eeed94e">&quot;Introduction to Bayesian Networks,&quot; Devin Soni - <em>Towards Data Science</em></a></li>
<li><a href="https://machinelearningmastery.com/introduction-to-bayesian-belief-networks/">&quot;A Gentle Introduction to Bayesian Belief Networks,&quot; Jason Brownlee - <em>Machine Learning Mastery</em></a></li>
</ul>
<p><strong>Code</strong> (alphabetical)</p>
<ul>
<li><a href="http://www.bnlearn.com">bnlearn</a> - routines for learning and inference in <code>R</code>.</li>
<li><a href="https://libra.cs.uoregon.edu">Libra Toolkit</a> - A collection of algorithms for learning and inference with discrete probabilistic models in <code>OCaml</code>.</li>
<li><a href="https://pomegranate.readthedocs.io/en/latest/index.html">Pomegranate</a> - routines for learning and inference in <code>Python</code> (<a href="https://github.com/jmschrei/pomegranate">Repository</a>).</li>
</ul>
<h2 id="further-reading">Further Reading</h2>
<p><em>Topics not explicitly covered here, but related:</em></p>
<ul>
<li>Influence Diagrams</li>
<li>Causal Models</li>
<li><a href="https://github.com/arranger1044/awesome-spn">Sum-Product Networks / Arithmetic Circuits</a></li>
</ul>
<h2 id="license">License</h2>
<p><strong>awesome-bayes-nets</strong> is released under a <a href="https://creativecommons.org/publicdomain/zero/1.0/"><code>CC0</code></a>: a <em>Creative Commons 1.0 Universal (CC0 1.0) Public Domain Dedication.</em></p>
<p><a href="https://creativecommons.org/publicdomain/zero/1.0"><img src="data:image/svg+xml;base64,<?xml version="1.0" encoding="utf-8"?>
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