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. Anirudhan Badrinath, Frederic Wang, Zachary Pardos: pyBKT: An Accessible Python Library of Bayesian Knowledge Tracing Models (2021) arXiv
  2. Cros, Marie-Josée; Aubertot, Jean-Noël; Gaba, Sabrina; Reboud, Xavier; Sabbadin, Régis; Peyrard, Nathalie: Improving pest monitoring networks using a simulation-based approach to contribute to pesticide reduction (2021)
  3. Halbersberg, Dan; Wienreb, Maydan; Lerner, Boaz: Joint maximization of accuracy and information for learning the structure of a Bayesian network classifier (2020)
  4. Ramsey, Joseph D.; Malinsky, Daniel; Bui, Kevin V.: algcomparison: comparing the performance of graphical structure learning algorithms with TETRAD (2020)
  5. Qi, Xiaolong; Fan, Xiaocong; Gao, Yang; Liu, Yanfang: Learning Bayesian network structures using weakest mutual-information-first strategy (2019)
  6. Alaa, Ahmed M.; van der Schaar, Mihaela: A hidden absorbing semi-Markov model for informatively censored temporal data: learning and inference (2018)
  7. Chong, Carsten; Klüppelberg, Claudia: Contagion in financial systems: a Bayesian network approach (2018)
  8. Liu, Xuqing; Liu, Xinsheng: Structure learning of Bayesian networks by continuous particle swarm optimization algorithms (2018)
  9. Liu, Xu-Qing; Liu, Xin-Sheng: Markov blanket and Markov boundary of multiple variables (2018)
  10. Zhang, Wenyu; Zhang, Zhenjiang; Chao, Han-Chieh; Tseng, Fan-Hsun: Kernel mixture model for probability density estimation in Bayesian classifiers (2018)
  11. Zhang, Xiangyin; Xue, Yuying; Lu, Xingyang; Jia, Songmin: Differential-evolution-based coevolution ant colony optimization algorithm for Bayesian network structure learning (2018)
  12. Warrell, Jonathan; Mhlanga, Musa: Stability and structural properties of gene regulation networks with coregulation rules (2017)
  13. Boreale, Michele; Corradi, Fabio: Searching secrets rationally (2016)
  14. Bouhamed, Heni; Masmoudi, Afif; Lecroq, Thierry; Rebaï, Ahmed: Structure space of Bayesian networks is dramatically reduced by subdividing it in sub-networks (2015)
  15. Bouhamed, Heni; Masmoudi, Afif; Lecroq, Thierry; Rebaï, Ahmed: Reducing the structure space of Bayesian classifiers using some general algorithms (2015)
  16. Kohler, Dominic; Marzouk, Youssef M.; Müller, Johannes; Wever, Utz: A new network approach to Bayesian inference in partial differential equations (2015)
  17. Kwisthout, Johan: Most frugal explanations in Bayesian networks (2015)
  18. Li, Yanying; Yang, Youlong; Zhu, Xiaofeng; Yang, Wenming: Towards a fast and efficient algorithm for learning Bayesian network (2015)
  19. Lowd, Daniel; Rooshenas, Amirmohammad: The Libra toolkit for probabilistic models (2015)
  20. Codecasa, Daniele; Stella, Fabio: Learning continuous time Bayesian network classifiers (2014)

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