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Kevin Murphy's tutorial, including a recommended reading list.
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A survey and tutorial by Daryle Niedermayer - covers material on Bayesian inference in general and selected industrial applications of graphical models
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A free, interactive tutorial on Bayesian modeling, in particular dependence and classification modeling.
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Maintained by Gal Elidan - over a dozen publicly available networks with documentation, in several popular interchange formats
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Dynamic Trees are mixtures of tree structured belief networks, and are used as models for image segmentation and tracking.
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Software, publications, teaching material, and news on belief revision - from the Business and Technology Research Laboratory at the University of Newcastle, Australia
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Briefing document to facilitate understanding Bayesian statistics, including its application to intelligence quotient statistics.
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Daphne Koller's research group on probabilistic representation, reasoning, and learning at Stanford University
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Research group at the University of Pittsburgh with links to books and software on probabilistic, decision-theoretic, and econometric graphical models
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Probabilistic reasoning and genetic algorithms for perception, inference and action: Bayesian cognitive and brain models, software for robotics, probabilistic inference engine
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Slides and additional notes from a tutorial by Nir Friedman and Daphne Koller on automated learning of belief networks, given at the Neural Information Processing Systems (NIPS-2001) conference
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Paper about combining probabilistic models and human-intuitive approaches to modeling uncertainty by generating qualitative verbal explanations of reasoning.
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Article published in JAIR (Journal of AI Research) about a way to implement belief networks by compiling networks into arithmetic expressions and then answering queries using an evaluation algorithm.
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Main conference for Belief Networks.
