Probabilistic reasoning and genetic algorithms for perception, inference and action: Bayesian cognitive and brain models, software for robotics, probabilistic inference engine

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

A survey and tutorial by Daryle Niedermayer - covers material on Bayesian inference in general and selected industrial applications of graphical models

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

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

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

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

Briefing document with a short survey of Bayesian statistics

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

Kevin Murphy's tutorial, including a recommended reading list.