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https://github.com/hayesall/awesome-bayes-nets

⚗️ A curated list of Books, Research Papers, and Software for Bayesian Networks.
https://github.com/hayesall/awesome-bayes-nets
List: awesome-bayes-nets
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⚗️ A curated list of Books, Research Papers, and Software for Bayesian Networks.
Host: GitHub
URL: https://github.com/hayesall/awesome-bayes-nets
Owner: hayesall
License: cc0-1.0
Created: 2019-04-17T02:41:35.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2024-04-06T11:53:16.000Z (9 months ago)
Last Synced: 2024-04-06T12:36:50.338Z (9 months ago)
Topics: awesome, awesome-list, bayes, bayesian-networks, machine-learning, probabilistic-graphical-models
Language: Python
Homepage: https://hayesall.com/awesome-bayes-nets/
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Stars: 10
Watchers: 1
Forks: 3
Open Issues: 0
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
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README

        


  
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`](https://github.com/arranger1044/awesome-spn) repository, which

in turn was inspired by the [SPN page](http://spn.cs.washington.edu/) at the

University of Washington. Some inspiration was also drawn from the original

[Bayesian Network Repository](http://www.cs.huji.ac.il/~galel/Repository/)

by Gal Elidan and Nir Friedman.

## Contributing

We have adopted the [*Contributor Code of Covenant*](.github/CODE_OF_CONDUCT.md).

Contributions are appreciated, but please read the

[`CONTRIBUTING.md`](CONTRIBUTING.md) and follow the guidelines provided

for issues and pull requests.

[Alexander L. Hayes](https://hayesall.com/) currently maintains this list.

He is notified when new

[issues](https://github.com/batflyer/awesome-bayes-nets/issues) or

[pull requests](https://github.com/batflyer/awesome-bayes-nets/pulls) are

submitted, but may not always respond immediately. He can also be reached at

[`[email protected]`](mailto:[email protected]).

---

## Contents

*Do we need a New Topic?* See [here](CONTRIBUTING.md#new-topics).

1. [Papers by Year](#papers-by-year)

    - [2018](#2018)

    - [2017](#2017)

    - [2016](#2016)

    - [2015](#2015)

    - [2010](#2010)

    - [2002](#2002)

    - [2000](#2000)

    - [1999](#1999)

    - [1998](#1998)

    - [1997](#1997)

    - [1996](#1996)

    - [1995](#1995)

    - [1994](#1994)

    - [1993](#1993)

    - [1992](#1992)

    - [1979](#1979)

    - [1968](#1968)

2. [Papers by Topic](#papers-by-topic)

    - [structure-learning](#structure-learning)

    - [structure-and-parameter-learning](#structure-and-parameter-learning)

    - [applications](#applications)

    - [theory](#theory)

3. [Resources](#resources)

4. [Further Reading](#further-reading)

## Papers by Year

### 2018

- Jacob Schreiber. (2018). "[pomegranate: Fast and Flexible Probabilistic Modeling in Python](http://jmlr.org/papers/v18/17-636.html)." Journal of Machine Learning Research. [`2018_schreiber.bib`](bib/2018/2018_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`](bib/2017/2017_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](https://doi.org/10.1145/2858036.2858221)." Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. [`2016_gorinova.bib`](bib/2016/2016_gorinova.bib)

### 2015

- Lowd, Daniel and Rooshenas, Amirmohammad. (2015). "[The Libra Toolkit for Probabilistic Models](http://www.jmlr.org/papers/volume16/lowd15a/lowd15a.pdf)." The Journal of Machine Learning Research. [`2015_lowd.bib`](bib/2015/2015_lowd.bib)

### 2010

- Gopalakrishnan, Vanathi and Lustgarten, Jonathan L. and Visweswaran, Shyam and Cooper, Gregory F.. (2010). "[Bayesian rule learning for biomedical data mining](https://doi.org/10.1093/bioinformatics/btq005)." Bioinformatics. [`2010_gopalakrishnan.bib`](bib/2010/2010_gopalakrishnan.bib)

### 2002

- Lerner, Uri N. (2002). "[Hybrid Bayesian Networks for Reasoning about Complex Systems](https://pdfs.semanticscholar.org/5609/16ef9bf3dffee6bd74192b5987870a66fad7.pdf)." Ph.D. Thesis. [`2002_lerner.bib`](bib/2002/2002_lerner.bib)

- Chickering, David Maxwell. (2002). "[Learning Equivalence Classes of Bayesian-Network Structures](http://www.jmlr.org/papers/volume2/chickering02a/chickering02a.pdf)." Journal of Machine Learning Research. [`2002_chickering.bib`](bib/2002/2002_chickering.bib)

### 2000

- Friedman, Nir and Linial, Michal and Nachman, Iftach and Pe'er, Dana. (2000). "[Using Bayesian Networks to Analyze Expression Data](https://doi.org/10.1145/332306.332355)." Proceedings of the Fourth Annual International Conference on Computational Molecular Biology. [`2000_friedman.bib`](bib/2000/2000_friedman.bib)

- Tian, Jin. (2000). "[A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks](https://dl.acm.org/doi/abs/10.5555/2073946.2074014)." Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence. [`2000_tian.bib`](bib/2000/2000_tian.bib)

### 1999

- Friedman, Nir and Nachman, Iftach and Peér, Dana. (1999). "[Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm](https://arxiv.org/abs/1301.6696)." Proceedings of the Fifteenth conference on Uncertainty in Artificial Intelligence (UAI). [`1999_friedman.bib`](bib/1999/1999_friedman.bib)

- David Heckerman. (1999). "A Tutorial on Learning with Bayesian Networks." Learning in Graphical Models. [`1999_heckerman.bib`](bib/1999/1999_heckerman.bib)

### 1998

- Ghahramani, Zoubin. (1998). "[Learning Dynamic Bayesian Networks](https://doi.org/10.1007/BFb0053999)." 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`](bib/1998/1998_ghahramani.bib)

- Shachter, Ross D.. (1998). "[Bayes-Ball: The Rational Pastime (for Determining Irrelevance and Requisite Information in Belief Networks and Influence Diagrams)](https://arxiv.org/abs/1301.7412)." Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI). [`1998_shachter.bib`](bib/1998/1998_shachter.bib)

### 1997

- Friedman, Nir and Geiger, Dan and Goldszmidt, Moises. (1997). "[Bayesian Network Classifiers](https://doi.org/10.1023/A:1007465528199)." Machine Learning. [`1997_friedman.bib`](bib/1997/1997_friedman.bib)

### 1996

- Chickering, David Maxwell. (1996). "[Learning Bayesian Networks is NP-Complete](https://doi.org/10.1007/978-1-4612-2404-4_12)." Learning from Data: Artificial Intelligence and Statistics V. [`1996_chickering.bib`](bib/1996/1996_chickering.bib)

- Sahami, Mehran. (1996). "[Learning Limited Dependence Bayesian Classifiers](https://www.aaai.org/Papers/KDD/1996/KDD96-061.pdf)." Knowledge Discovery and Data Mining (KDD). [`1996_sahami.bib`](bib/1996/1996_sahami.bib)

### 1995

- Chickering, David Maxwell. (1995). "[A Transformational Characterization of Equivalent Bayesian Network Structures](https://arxiv.org/abs/1302.4938)." Proceedings of the Eleventh conference on Uncertainty in Artificial Intelligence (UAI). [`1995_chickering.bib`](bib/1995/1995_chickering.bib)

- Heckerman, David and Geiger, Dan and Chickering, David M. (1995). "[Learning Bayesian Networks: The Combination of Knowledge and Statistical Data](https://doi.org/10.1023/A:1022623210503)." Machine Learning. [`1995_heckerman.bib`](bib/1995/1995_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](https://arxiv.org/abs/1302.4945)." Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI). [`1995_ezawa.bib`](bib/1995/1995_ezawa.bib)

- Bouckaert, Remco Ronaldus. (1995). "[Bayesian Belief Networks: From Construction to Inference](https://dspace.library.uu.nl/handle/1874/845)." Ph.D. Thesis. [`1995_bouckaert.bib`](bib/1995/1995_bouckaert.bib)

### 1994

- Lam, Wai and Bacchus, Fahiem. (1994). "[Learning Bayesian Belief Networks: An Approach Based on the MDL Principle](https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-8640.1994.tb00166.x)." Computational Intelligence. [`1994_lam.bib`](bib/1994/1994_lam.bib)

### 1993

- Bouckaert, Remco R.. (1993). "[Probabilistic network construction using the minimum description length principle](https://doi.org/10.1007/BFb0028180)." Symbolic and Quantitative Approaches to Reasoning and Uncertainty. [`1993_bouckaert.bib`](bib/1993/1993_bouckaert.bib)

### 1992

- Cooper, Gregory F. and Herskovits, Edward. (1992). "[A Bayesian Method for the Induction of Probabilistic Networks from Data](https://doi.org/10.1007/BF00994110)." Machine Learning. [`1992_cooper.bib`](bib/1992/1992_cooper.bib)

### 1979

- Rijsbergen, C. J. Van. (1979). "[Information Retrieval, 2nd Edition](http://www.dcs.gla.ac.uk/Keith/Preface.html)." Butterworths. [`1979_rijsbergen.bib`](bib/1979/1979_rijsbergen.bib)

### 1968

- C. Chow and C. Liu. (1968). "[Approximating Discrete Probability Distributions with Dependence Trees](https://doi.org/10.1109/TIT.1968.1054142)." IEEE Transactions on Information Theory. [`1968_chow.bib`](bib/1968/1968_chow.bib)

## Papers by Topic

### structure-learning

- Bouckaert, Remco R.. (1993). "[Probabilistic network construction using the minimum description length principle](https://doi.org/10.1007/BFb0028180)." Symbolic and Quantitative Approaches to Reasoning and Uncertainty. [`1993_bouckaert.bib`](bib/1993/1993_bouckaert.bib)

- Cooper, Gregory F. and Herskovits, Edward. (1992). "[A Bayesian Method for the Induction of Probabilistic Networks from Data](https://doi.org/10.1007/BF00994110)." Machine Learning. [`1992_cooper.bib`](bib/1992/1992_cooper.bib)

- Chickering, David Maxwell. (1995). "[A Transformational Characterization of Equivalent Bayesian Network Structures](https://arxiv.org/abs/1302.4938)." Proceedings of the Eleventh conference on Uncertainty in Artificial Intelligence (UAI). [`1995_chickering.bib`](bib/1995/1995_chickering.bib)

- Heckerman, David and Geiger, Dan and Chickering, David M. (1995). "[Learning Bayesian Networks: The Combination of Knowledge and Statistical Data](https://doi.org/10.1023/A:1022623210503)." Machine Learning. [`1995_heckerman.bib`](bib/1995/1995_heckerman.bib)

- Chickering, David Maxwell. (2002). "[Learning Equivalence Classes of Bayesian-Network Structures](http://www.jmlr.org/papers/volume2/chickering02a/chickering02a.pdf)." Journal of Machine Learning Research. [`2002_chickering.bib`](bib/2002/2002_chickering.bib)

- Tian, Jin. (2000). "[A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks](https://dl.acm.org/doi/abs/10.5555/2073946.2074014)." Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence. [`2000_tian.bib`](bib/2000/2000_tian.bib)

- Sahami, Mehran. (1996). "[Learning Limited Dependence Bayesian Classifiers](https://www.aaai.org/Papers/KDD/1996/KDD96-061.pdf)." Knowledge Discovery and Data Mining (KDD). [`1996_sahami.bib`](bib/1996/1996_sahami.bib)

- Lam, Wai and Bacchus, Fahiem. (1994). "[Learning Bayesian Belief Networks: An Approach Based on the MDL Principle](https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-8640.1994.tb00166.x)." Computational Intelligence. [`1994_lam.bib`](bib/1994/1994_lam.bib)

- Friedman, Nir and Nachman, Iftach and Peér, Dana. (1999). "[Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm](https://arxiv.org/abs/1301.6696)." Proceedings of the Fifteenth conference on Uncertainty in Artificial Intelligence (UAI). [`1999_friedman.bib`](bib/1999/1999_friedman.bib)

### structure-and-parameter-learning

- Jacob Schreiber. (2018). "[pomegranate: Fast and Flexible Probabilistic Modeling in Python](http://jmlr.org/papers/v18/17-636.html)." Journal of Machine Learning Research. [`2018_schreiber.bib`](bib/2018/2018_schreiber.bib)

- Ghahramani, Zoubin. (1998). "[Learning Dynamic Bayesian Networks](https://doi.org/10.1007/BFb0053999)." 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`](bib/1998/1998_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`](bib/2017/2017_schreiber.bib)

- Friedman, Nir and Geiger, Dan and Goldszmidt, Moises. (1997). "[Bayesian Network Classifiers](https://doi.org/10.1023/A:1007465528199)." Machine Learning. [`1997_friedman.bib`](bib/1997/1997_friedman.bib)

- Lowd, Daniel and Rooshenas, Amirmohammad. (2015). "[The Libra Toolkit for Probabilistic Models](http://www.jmlr.org/papers/volume16/lowd15a/lowd15a.pdf)." The Journal of Machine Learning Research. [`2015_lowd.bib`](bib/2015/2015_lowd.bib)

- David Heckerman. (1999). "A Tutorial on Learning with Bayesian Networks." Learning in Graphical Models. [`1999_heckerman.bib`](bib/1999/1999_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](https://arxiv.org/abs/1302.4945)." Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI). [`1995_ezawa.bib`](bib/1995/1995_ezawa.bib)

- Friedman, Nir and Linial, Michal and Nachman, Iftach and Pe'er, Dana. (2000). "[Using Bayesian Networks to Analyze Expression Data](https://doi.org/10.1145/332306.332355)." Proceedings of the Fourth Annual International Conference on Computational Molecular Biology. [`2000_friedman.bib`](bib/2000/2000_friedman.bib)

- Gopalakrishnan, Vanathi and Lustgarten, Jonathan L. and Visweswaran, Shyam and Cooper, Gregory F.. (2010). "[Bayesian rule learning for biomedical data mining](https://doi.org/10.1093/bioinformatics/btq005)." Bioinformatics. [`2010_gopalakrishnan.bib`](bib/2010/2010_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](https://doi.org/10.1145/2858036.2858221)." Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. [`2016_gorinova.bib`](bib/2016/2016_gorinova.bib)

### theory

- Bouckaert, Remco Ronaldus. (1995). "[Bayesian Belief Networks: From Construction to Inference](https://dspace.library.uu.nl/handle/1874/845)." Ph.D. Thesis. [`1995_bouckaert.bib`](bib/1995/1995_bouckaert.bib)

- Shachter, Ross D.. (1998). "[Bayes-Ball: The Rational Pastime (for Determining Irrelevance and Requisite Information in Belief Networks and Influence Diagrams)](https://arxiv.org/abs/1301.7412)." Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI). [`1998_shachter.bib`](bib/1998/1998_shachter.bib)

- C. Chow and C. Liu. (1968). "[Approximating Discrete Probability Distributions with Dependence Trees](https://doi.org/10.1109/TIT.1968.1054142)." IEEE Transactions on Information Theory. [`1968_chow.bib`](bib/1968/1968_chow.bib)

- Lerner, Uri N. (2002). "[Hybrid Bayesian Networks for Reasoning about Complex Systems](https://pdfs.semanticscholar.org/5609/16ef9bf3dffee6bd74192b5987870a66fad7.pdf)." Ph.D. Thesis. [`2002_lerner.bib`](bib/2002/2002_lerner.bib)

- Chickering, David Maxwell. (1996). "[Learning Bayesian Networks is NP-Complete](https://doi.org/10.1007/978-1-4612-2404-4_12)." Learning from Data: Artificial Intelligence and Statistics V. [`1996_chickering.bib`](bib/1996/1996_chickering.bib)

- Rijsbergen, C. J. Van. (1979). "[Information Retrieval, 2nd Edition](http://www.dcs.gla.ac.uk/Keith/Preface.html)." Butterworths. [`1979_rijsbergen.bib`](bib/1979/1979_rijsbergen.bib)

## Resources

**Blog Posts and Short Overviews**

- ["A Brief Introduction to Graphical Models and Bayesian Networks," Kevin Murphy](https://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html)

- ["Directed Graphical Models," Nicholas Ruozzi](https://personal.utdallas.edu/~nrr150130/gmbook/bayes.html)

- ["Bayesian networks," Stefano Ermon](https://ermongroup.github.io/cs228-notes/representation/directed/)

- ["Introduction to Bayesian Networks," Devin Soni - *Towards Data Science*](https://towardsdatascience.com/introduction-to-bayesian-networks-81031eeed94e)

- ["A Gentle Introduction to Bayesian Belief Networks," Jason Brownlee - *Machine Learning Mastery*](https://machinelearningmastery.com/introduction-to-bayesian-belief-networks/)

**Code** (alphabetical)

- [bnlearn](http://www.bnlearn.com) - routines for learning and inference in `R`.

- [Libra Toolkit](https://libra.cs.uoregon.edu) - A collection of algorithms for learning and inference with discrete probabilistic models in `OCaml`.

- [Pomegranate](https://pomegranate.readthedocs.io/en/latest/index.html) - routines for learning and inference in `Python` ([Repository](https://github.com/jmschrei/pomegranate)).

## Further Reading

*Topics not explicitly covered here, but related:*

- Influence Diagrams

- Causal Models

- [Sum-Product Networks / Arithmetic Circuits](https://github.com/arranger1044/awesome-spn)

## License

**awesome-bayes-nets** is released under a

[`CC0`](https://creativecommons.org/publicdomain/zero/1.0/): a *Creative Commons

1.0 Universal (CC0 1.0) Public Domain Dedication.*

[![CC0](https://mirrors.creativecommons.org/presskit/buttons/88x31/svg/cc-zero.svg)](https://creativecommons.org/publicdomain/zero/1.0)