awesome-bayes-nets

_#bayesrocks

awesome-bayes-nets is a curated and structured list of Books, Research Papers, and Software for Bayesian Networks (BNs).

Papers are sorted by year and topics. This was inspired (and modeled on) Antonio Vergari's awesome-spn repository, which in turn was inspired by the SPN page at the University of Washington. Some inspiration was also drawn from the original Bayesian Network Repository by Gal Elidan and Nir Friedman.

Contributing

We have adopted the Contributor Code of Covenant. Contributions are appreciated, but please read the CONTRIBUTING.md and follow the guidelines provided for issues and pull requests.

Alexander L. Hayes currently maintains this list. He is notified when new issues or pull requests are submitted, but may not always respond immediately. He can also be reached at hayesall@iu.edu.

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Contents

Do we need a New Topic? See here.

1. Papers by Year
   • 2018
   • 2017
   • 2016
   • 2015
   • 2010
   • 2002
   • 2000
   • 1999
   • 1998
   • 1997
   • 1996
   • 1995
   • 1994
   • 1993
   • 1992
   • 1979
   • 1968
2. Papers by Topic
   • structure-learning
   • structure-and-parameter-learning
   • applications
   • theory
3. Resources
4. Further Reading

Papers by Year

2018

• Jacob Schreiber. (2018). "pomegranate: Fast and Flexible Probabilistic Modeling in Python." Journal of Machine Learning Research. 2018_schreiber.bib

2017

• Schreiber, Jacob M and Noble, William S. (2017). "Finding the optimal Bayesian network given a constraint graph." PeerJ Computer Science. 2017_schreiber.bib

2016

• Gorinova, Maria I. and Sarkar, Advait and Blackwell, Alan F. and Syme, Don. (2016). "A Live, Multiple-Representation Probabilistic Programming Environment for Novices." Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. 2016_gorinova.bib

2015

• Lowd, Daniel and Rooshenas, Amirmohammad. (2015). "The Libra Toolkit for Probabilistic Models." The Journal of Machine Learning Research. 2015_lowd.bib

2010

• Gopalakrishnan, Vanathi and Lustgarten, Jonathan L. and Visweswaran, Shyam and Cooper, Gregory F.. (2010). "Bayesian rule learning for biomedical data mining." Bioinformatics. 2010_gopalakrishnan.bib

2002

• Lerner, Uri N. (2002). "Hybrid Bayesian Networks for Reasoning about Complex Systems." Ph.D. Thesis. 2002_lerner.bib
• Chickering, David Maxwell. (2002). "Learning Equivalence Classes of Bayesian-Network Structures." Journal of Machine Learning Research. 2002_chickering.bib

2000

• Friedman, Nir and Linial, Michal and Nachman, Iftach and Pe'er, Dana. (2000). "Using Bayesian Networks to Analyze Expression Data." Proceedings of the Fourth Annual International Conference on Computational Molecular Biology. 2000_friedman.bib
• Tian, Jin. (2000). "A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks." Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence. 2000_tian.bib

1999

• Friedman, Nir and Nachman, Iftach and Peér, Dana. (1999). "Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm." Proceedings of the Fifteenth conference on Uncertainty in Artificial Intelligence (UAI). 1999_friedman.bib
• David Heckerman. (1999). "A Tutorial on Learning with Bayesian Networks." Learning in Graphical Models. 1999_heckerman.bib

1998

• Ghahramani, Zoubin. (1998). "Learning Dynamic Bayesian Networks." 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. 1998_ghahramani.bib
• Shachter, Ross D.. (1998). "Bayes-Ball: The Rational Pastime (for Determining Irrelevance and Requisite Information in Belief Networks and Influence Diagrams)." Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI). 1998_shachter.bib

1997

• Friedman, Nir and Geiger, Dan and Goldszmidt, Moises. (1997). "Bayesian Network Classifiers." Machine Learning. 1997_friedman.bib

1996

• Chickering, David Maxwell. (1996). "Learning Bayesian Networks is NP-Complete." Learning from Data: Artificial Intelligence and Statistics V. 1996_chickering.bib
• Sahami, Mehran. (1996). "Learning Limited Dependence Bayesian Classifiers." Knowledge Discovery and Data Mining (KDD). 1996_sahami.bib

1995

• Chickering, David Maxwell. (1995). "A Transformational Characterization of Equivalent Bayesian Network Structures." Proceedings of the Eleventh conference on Uncertainty in Artificial Intelligence (UAI). 1995_chickering.bib
• Heckerman, David and Geiger, Dan and Chickering, David M. (1995). "Learning Bayesian Networks: The Combination of Knowledge and Statistical Data." Machine Learning. 1995_heckerman.bib
• Ezawa, Kazuo J. and Schuermann, Til. (1995). "Fraud/Uncollectible Debt Detection Using a Bayesian Network Based Learning System: A Rare Binary Outcome with Mixed Data Structures." Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI). 1995_ezawa.bib
• Bouckaert, Remco Ronaldus. (1995). "Bayesian Belief Networks: From Construction to Inference." Ph.D. Thesis. 1995_bouckaert.bib

1994

• Lam, Wai and Bacchus, Fahiem. (1994). "Learning Bayesian Belief Networks: An Approach Based on the MDL Principle." Computational Intelligence. 1994_lam.bib

1993

• Bouckaert, Remco R.. (1993). "Probabilistic network construction using the minimum description length principle." Symbolic and Quantitative Approaches to Reasoning and Uncertainty. 1993_bouckaert.bib

1992

• Cooper, Gregory F. and Herskovits, Edward. (1992). "A Bayesian Method for the Induction of Probabilistic Networks from Data." Machine Learning. 1992_cooper.bib

1979

• Rijsbergen, C. J. Van. (1979). "Information Retrieval, 2nd Edition." Butterworths. 1979_rijsbergen.bib

1968

• C. Chow and C. Liu. (1968). "Approximating Discrete Probability Distributions with Dependence Trees." IEEE Transactions on Information Theory. 1968_chow.bib

Papers by Topic

structure-learning

• Bouckaert, Remco R.. (1993). "Probabilistic network construction using the minimum description length principle." Symbolic and Quantitative Approaches to Reasoning and Uncertainty. 1993_bouckaert.bib
• Cooper, Gregory F. and Herskovits, Edward. (1992). "A Bayesian Method for the Induction of Probabilistic Networks from Data." Machine Learning. 1992_cooper.bib
• Chickering, David Maxwell. (1995). "A Transformational Characterization of Equivalent Bayesian Network Structures." Proceedings of the Eleventh conference on Uncertainty in Artificial Intelligence (UAI). 1995_chickering.bib
• Heckerman, David and Geiger, Dan and Chickering, David M. (1995). "Learning Bayesian Networks: The Combination of Knowledge and Statistical Data." Machine Learning. 1995_heckerman.bib
• Chickering, David Maxwell. (2002). "Learning Equivalence Classes of Bayesian-Network Structures." Journal of Machine Learning Research. 2002_chickering.bib
• Tian, Jin. (2000). "A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks." Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence. 2000_tian.bib
• Sahami, Mehran. (1996). "Learning Limited Dependence Bayesian Classifiers." Knowledge Discovery and Data Mining (KDD). 1996_sahami.bib
• Lam, Wai and Bacchus, Fahiem. (1994). "Learning Bayesian Belief Networks: An Approach Based on the MDL Principle." Computational Intelligence. 1994_lam.bib
• Friedman, Nir and Nachman, Iftach and Peér, Dana. (1999). "Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm." Proceedings of the Fifteenth conference on Uncertainty in Artificial Intelligence (UAI). 1999_friedman.bib

structure-and-parameter-learning

• Jacob Schreiber. (2018). "pomegranate: Fast and Flexible Probabilistic Modeling in Python." Journal of Machine Learning Research. 2018_schreiber.bib
• Ghahramani, Zoubin. (1998). "Learning Dynamic Bayesian Networks." 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. 1998_ghahramani.bib
• Schreiber, Jacob M and Noble, William S. (2017). "Finding the optimal Bayesian network given a constraint graph." PeerJ Computer Science. 2017_schreiber.bib
• Friedman, Nir and Geiger, Dan and Goldszmidt, Moises. (1997). "Bayesian Network Classifiers." Machine Learning. 1997_friedman.bib
• Lowd, Daniel and Rooshenas, Amirmohammad. (2015). "The Libra Toolkit for Probabilistic Models." The Journal of Machine Learning Research. 2015_lowd.bib
• David Heckerman. (1999). "A Tutorial on Learning with Bayesian Networks." Learning in Graphical Models. 1999_heckerman.bib

applications

• Ezawa, Kazuo J. and Schuermann, Til. (1995). "Fraud/Uncollectible Debt Detection Using a Bayesian Network Based Learning System: A Rare Binary Outcome with Mixed Data Structures." Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI). 1995_ezawa.bib
• Friedman, Nir and Linial, Michal and Nachman, Iftach and Pe'er, Dana. (2000). "Using Bayesian Networks to Analyze Expression Data." Proceedings of the Fourth Annual International Conference on Computational Molecular Biology. 2000_friedman.bib
• Gopalakrishnan, Vanathi and Lustgarten, Jonathan L. and Visweswaran, Shyam and Cooper, Gregory F.. (2010). "Bayesian rule learning for biomedical data mining." Bioinformatics. 2010_gopalakrishnan.bib
• Gorinova, Maria I. and Sarkar, Advait and Blackwell, Alan F. and Syme, Don. (2016). "A Live, Multiple-Representation Probabilistic Programming Environment for Novices." Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. 2016_gorinova.bib

theory

• Bouckaert, Remco Ronaldus. (1995). "Bayesian Belief Networks: From Construction to Inference." Ph.D. Thesis. 1995_bouckaert.bib
• Shachter, Ross D.. (1998). "Bayes-Ball: The Rational Pastime (for Determining Irrelevance and Requisite Information in Belief Networks and Influence Diagrams)." Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI). 1998_shachter.bib
• C. Chow and C. Liu. (1968). "Approximating Discrete Probability Distributions with Dependence Trees." IEEE Transactions on Information Theory. 1968_chow.bib
• Lerner, Uri N. (2002). "Hybrid Bayesian Networks for Reasoning about Complex Systems." Ph.D. Thesis. 2002_lerner.bib
• Chickering, David Maxwell. (1996). "Learning Bayesian Networks is NP-Complete." Learning from Data: Artificial Intelligence and Statistics V. 1996_chickering.bib
• Rijsbergen, C. J. Van. (1979). "Information Retrieval, 2nd Edition." Butterworths. 1979_rijsbergen.bib

Resources

Blog Posts and Short Overviews

• "A Brief Introduction to Graphical Models and Bayesian Networks," Kevin Murphy
• "Directed Graphical Models," Nicholas Ruozzi
• "Bayesian networks," Stefano Ermon
• "Introduction to Bayesian Networks," Devin Soni - Towards Data Science
• "A Gentle Introduction to Bayesian Belief Networks," Jason Brownlee - Machine Learning Mastery

Code (alphabetical)

• bnlearn - routines for learning and inference in R.
• Libra Toolkit - A collection of algorithms for learning and inference with discrete probabilistic models in OCaml.
• Pomegranate - routines for learning and inference in Python (Repository).

Further Reading

Topics not explicitly covered here, but related:

• Influence Diagrams
• Causal Models
• Sum-Product Networks / Arithmetic Circuits

License

awesome-bayes-nets is released under a CC0: a Creative Commons 1.0 Universal (CC0 1.0) Public Domain Dedication.