The Bayes Net Toolbox (BNT) is an open-source Matlab package for directed graphical models. BNT supports many kinds of nodes (probability distributions), exact and approximate inference, parameter and structure learning, and static and dynamic models. BNT is widely used in teaching and research: the web page has received over 28,000 hits since May 2000. In this paper, we discuss a broad spectrum of issues related to graphical models (directed and undirected), and describe, at a high-level, how BNT was designed to cope with them all. We also compare BNT to other software packages for graphical models, and to the nascent OpenBayes effort.

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  1. Boreale, Michele; Corradi, Fabio: Searching secrets rationally (2016)
  2. del Sagrado, J.; Sánchez, J.A.; Rodríguez, F.; Berenguel, M.: Bayesian networks for greenhouse temperature control (2016)
  3. Bouhamed, Heni; Masmoudi, Afif; Lecroq, Thierry; Rebaï, Ahmed: Reducing the structure space of Bayesian classifiers using some general algorithms (2015)
  4. Bouhamed, Heni; Masmoudi, Afif; Lecroq, Thierry; Rebaï, Ahmed: Structure space of Bayesian networks is dramatically reduced by subdividing it in sub-networks (2015)
  5. Kwisthout, Johan: Most frugal explanations in Bayesian networks (2015)
  6. Li, Yanying; Yang, Youlong; Zhu, Xiaofeng; Yang, Wenming: Towards a fast and efficient algorithm for learning Bayesian network (2015)
  7. Lowd, Daniel; Rooshenas, Amirmohammad: The Libra toolkit for probabilistic models (2015)
  8. Codecasa, Daniele; Stella, Fabio: Learning continuous time Bayesian network classifiers (2014)
  9. Marshall, Adele H.; Shaw, Barry: Computational learning of the conditional phase-type (C-Ph) distribution. Learning C-Ph distributions (2014)
  10. Bacciu, Davide; Etchells, Terence A.; Lisboa, Paulo J.G.; Whittaker, Joe: Efficient identification of independence networks using mutual information (2013)
  11. Hernandez-Leal, Pablo; Gonzalez, Jesus A.; Morales, Eduardo F.; Enrique Sucar, L.: Learning temporal nodes Bayesian networks (2013)
  12. Liu, Sanyang; Zhu, Mingmin; Yang, Youlong: A Bayesian classifier learning algorithm based on optimization model (2013)
  13. Pisharady, Pramod Kumar; Vadakkepat, Prahlad; Loh, Ai Poh: Attention based detection and recognition of hand postures against complex backgrounds (2013) ioport
  14. Kelner, Roy; Lerner, Boaz: Learning Bayesian network classifiers by risk minimization (2012)
  15. Zhu, Mingmin; Liu, Sanyang: A decomposition algorithm for learning Bayesian networks based on scoring function (2012)
  16. Jensen, Finn V.; Nielsen, Thomas Dyhre: Probabilistic decision graphs for optimization under uncertainty (2011)
  17. Karwa, Vishesh; Slavković, Aleksandra B.; Donnell, Eric T.: Causal inference in transportation safety studies: comparison of potential outcomes and causal diagrams (2011)
  18. Xu, Ping-Feng; Guo, Jianhua; Tang, Man-Lai: Structural learning for Bayesian networks by testing complete separators in prime blocks (2011)
  19. Junejo, Imran N.: Using dynamic Bayesian network for scene modeling and anomaly detection (2010)
  20. Lee, Namgil; Kim, Jong-Min: Conversion of categorical variables into numerical variables via Bayesian network classifiers for binary classifications (2010)

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