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 72 articles )

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  1. Wang, Junbai; Morigen: Bayespi - a new model to study protein-DNA interactions: a case study of condition-specific protein binding parameters for yeast transcription factors (2009) ioport
  2. Yao, Hongliang; Chang, Jian; Jiang, Caizi; Wang, Hao: Junction tree factored particle inference algorithm for multi-agent dynamic influence diagrams (2009)
  3. Yehezkel, Raanan; Lerner, Boaz: Bayesian network structure learning by recursive autonomy identification (2009)
  4. Zou, Cunlu; Feng, Jianfeng: Granger causality vs. Dynamic Bayesian network inference: a comparative study (2009) ioport
  5. Ellis, Byron; Wong, Wing Hung: Learning causal Bayesian network structures from experimental data (2008)
  6. Hoiem, Derek; Efros, Alexei A.; Hebert, Martial: Putting objects in perspective (2008)
  7. Lähdesmäki, Harri; Shmulevich, Ilya: Learning the structure of dynamic Bayesian networks from time series and steady state measurements (2008)
  8. Likforman-Sulem, Laurence; Sigelle, Marc: Recognition of degraded characters using dynamic Bayesian networks (2008)
  9. Rijmen, Frank; Vansteelandt, Kristof; De Boeck, Paul: Latent class models for diary method data: Parameter estimation by local computations (2008)
  10. Santos Costa, Vítor; Page, David; Cussens, James: CLP((\mathcalBN)): Constraint logic programming for probabilistic knowledge (2008)
  11. Wang, Kaijun; Zhang, Junying; Shen, Fengshan; Shi, Lingfeng: Adaptive learning of dynamic Bayesian networks with changing structures by detecting geometric structures of time series (2008) ioport
  12. Xie, Xianchao; Geng, Zhi: A recursive method for structural learning of directed acyclic graphs (2008)
  13. Yudelson, Michael V.; Medvedeva, Olga P.; Crowley, Rebecca S.: A multifactor approach to student model evaluation (2008) ioport
  14. He, Dong; Zhou, Dao; Zhou, Yanhong: Identifying transcription factor targets using enhanced Bayesian classifier (2007)
  15. Nott, Cameron; Ölçmen, Semih M.; Karr, Charles L.; Trevino, Luis C.: (SR-30) turbojet engine real-time sensor health monitoring using neural networks, and Bayesian belief networks (2007) ioport
  16. Raiko, Tapani; Valpola, Harri; Harva, Markus; Karhunen, Juha: Building blocks for variational Bayesian learning of latent variable models (2007)
  17. Ross, Brian J.; Zuviria, Eduardo: Evolving dynamic Bayesian networks with multi-objective genetic algorithms (2007) ioport
  18. Basak, Jayanta: Online adaptive decision trees: pattern classification and function approximation (2006)
  19. Dean, Thomas: Learning invariant features using inertial priors (2006)
  20. Desmarais, Michel C.; Meshkinfam, Peyman; Gagnon, Michel: Learned student models with item to item knowledge structures (2006) ioport