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 72 articles )

Showing results 61 to 72 of 72.
Sorted by year (citations)
  1. Fox, Maria; Ghallab, Malik; Infantes, Guillaume; Long, Derek: Robot introspection through learned hidden Markov models (2006)
  2. Grindlay, Graham; Helmbold, David: Modeling, analyzing, and synthesizing expressive piano performance with graphical models (2006) ioport
  3. Heskes, T.: Convexity arguments for efficient minimization of the Bethe and Kikuchi free energies (2006)
  4. Polanska, Joanna; Borys, Damian; Polanski, Andrzej: Node assignment problem in Bayesian networks (2006)
  5. Tahboub, Karim A.: Intelligent human-machine interaction based on dynamic Bayesian networks probabilistic intention recognition (2006) ioport
  6. Town, Christopher: Ontological inference for image and video analysis (2006) ioport
  7. Yanover, Chen; Meltzer, Talya; Weiss, Yair: Linear programming relaxations and belief propagation -- an empirical study (2006)
  8. Zhou, Yujia; Pahwa, Anil; Das, Sanjoy: Prediction of weather-related failures of overhead distribution feeders (2006)
  9. Baesens, Bart; Verstraeten, Geert; Van den Poel, Dirk; Egmont-Petersen, Michael; Van Kenhove, Patrick; Vanthienen, Jan: Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers. (2004)
  10. Feng, Gary: From eye movement to cognition: toward a general framework of inference. Comment on Liechty et al., 2003 (2003)
  11. Pernkopf, Franz; O’Leary, Paul: Floating search algorithm for structure learning of Bayesian network classifiers. (2003)
  12. Kersting, Kristian; De Raedt, Luc: Towards combining inductive logic programming with Bayesian networks (2001)