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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 [email protected].


Contents

Do we need a New Topic? See here.

  1. Papers by Year
  2. Papers by Topic
  3. Resources
  4. Further Reading

Papers by Year

2018

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

2015

2010

2002

2000

1999

1998

1997

1996

1995

1994

1993

1992

1979

1968

Papers by Topic

structure-learning

structure-and-parameter-learning

applications

theory

Resources

Blog Posts and Short Overviews

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:

License

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

CC0