Main association for belief network researchers. Runs the annual Uncertainty in Artificial Intelligence (UAI) conferences, and the UAI mailing list.
Paper about combining probabilistic models and human-intuitive approaches to modeling uncertainty by generating qualitative verbal explanations of reasoning.
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.
Dynamic Trees are mixtures of tree structured belief networks, and are used as models for image segmentation and tracking.
Kevin Murphy's tutorial, including a recommended reading list.
A free, interactive tutorial on Bayesian modeling, in particular dependence and classification modeling.
Briefing document with a short survey of Bayesian statistics
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
Software, publications, teaching material, and news on belief revision - from the Business and Technology Research Laboratory at the University of Newcastle, Australia
Daphne Koller's research group on probabilistic representation, reasoning, and learning at Stanford University