BNT

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.


References in zbMATH (referenced in 56 articles )

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  1. Warrell, Jonathan; Mhlanga, Musa: Stability and structural properties of gene regulation networks with coregulation rules (2017)
  2. Zhao, Gaochang; Wang, Xin; Zhang, Zhonghua; Han, Miao; Wei, Wei: Based on MWST+T-K2 algorithm to build Bayes classifier (2017)
  3. Boreale, Michele; Corradi, Fabio: Searching secrets rationally (2016)
  4. del Sagrado, J.; Sánchez, J.A.; Rodríguez, F.; Berenguel, M.: Bayesian networks for greenhouse temperature control (2016)
  5. Bouhamed, Heni; Masmoudi, Afif; Lecroq, Thierry; Rebaï, Ahmed: Structure space of Bayesian networks is dramatically reduced by subdividing it in sub-networks (2015)
  6. Bouhamed, Heni; Masmoudi, Afif; Lecroq, Thierry; Rebaï, Ahmed: Reducing the structure space of Bayesian classifiers using some general algorithms (2015)
  7. Kohler, Dominic; Marzouk, Youssef M.; Müller, Johannes; Wever, Utz: A new network approach to Bayesian inference in partial differential equations (2015)
  8. Kwisthout, Johan: Most frugal explanations in Bayesian networks (2015)
  9. Li, Yanying; Yang, Youlong; Zhu, Xiaofeng; Yang, Wenming: Towards a fast and efficient algorithm for learning Bayesian network (2015)
  10. Lowd, Daniel; Rooshenas, Amirmohammad: The Libra toolkit for probabilistic models (2015)
  11. Codecasa, Daniele; Stella, Fabio: Learning continuous time Bayesian network classifiers (2014)
  12. Marshall, Adele H.; Shaw, Barry: Computational learning of the conditional phase-type (C-Ph) distribution. Learning C-Ph distributions (2014)
  13. Bacciu, Davide; Etchells, Terence A.; Lisboa, Paulo J.G.; Whittaker, Joe: Efficient identification of independence networks using mutual information (2013)
  14. Hernandez-Leal, Pablo; Gonzalez, Jesus A.; Morales, Eduardo F.; Enrique Sucar, L.: Learning temporal nodes Bayesian networks (2013)
  15. Liu, Sanyang; Zhu, Mingmin; Yang, Youlong: A Bayesian classifier learning algorithm based on optimization model (2013)
  16. Pisharady, Pramod Kumar; Vadakkepat, Prahlad; Loh, Ai Poh: Attention based detection and recognition of hand postures against complex backgrounds (2013) ioport
  17. Kelner, Roy; Lerner, Boaz: Learning Bayesian network classifiers by risk minimization (2012)
  18. Zhu, Mingmin; Liu, Sanyang: A decomposition algorithm for learning Bayesian networks based on scoring function (2012)
  19. Jensen, Finn V.; Nielsen, Thomas Dyhre: Probabilistic decision graphs for optimization under uncertainty (2011)
  20. Karwa, Vishesh; Slavković, Aleksandra B.; Donnell, Eric T.: Causal inference in transportation safety studies: comparison of potential outcomes and causal diagrams (2011)

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