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.

Paper about combining probabilistic models and human-intuitive approaches to modeling uncertainty by generating qualitative verbal explanations of reasoning.

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

Research group at the University of Pittsburgh with links to books and software on probabilistic, decision-theoretic, and econometric graphical models

Daphne Koller's research group on probabilistic representation, reasoning, and learning at Stanford University

Briefing document with a short survey of Bayesian statistics

Software, publications, teaching material, and news on belief revision - from the Business and Technology Research Laboratory at the University of Newcastle, Australia

Dynamic Trees are mixtures of tree structured belief networks, and are used as models for image segmentation and tracking.

A free, interactive tutorial on Bayesian modeling, in particular dependence and classification modeling.

Maintained by Gal Elidan - over a dozen publicly available networks with documentation, in several popular interchange formats