| A Brief Introduction to Graphical Models and Bayesian Networks: Kevin Murphy's tutorial, including a recommended reading list. |
| An Introduction to Bayesian Networks and Their Contemporary Applications: A survey and tutorial by Daryle Niedermayer - covers material on Bayesian inference in general and selected industrial applications of graphical models |
| Association for Uncertainty in Artificial Intelligence: Main association for belief network researchers. Runs the annual Uncertainty in Artificial Intelligence (UAI) conferences, and the UAI mailing list. |
| B-Course - Dependence and classification modeling: A free, interactive tutorial on Bayesian modeling, in particular dependence and classification modeling. |
| Bayesian Network Repository: Maintained by Gal Elidan - over a dozen publicly available networks with documentation, in several popular interchange formats |
| Belief Networks and Variational Methods : Amos Storkey: Dynamic Trees are mixtures of tree structured belief networks, and are used as models for image segmentation and tracking. |
| Belief Revision: Software, publications, teaching material, and news on belief revision - from the Business and Technology Research Laboratory at the University of Newcastle, Australia |
| Cause, chance and Bayesian statistics: Briefing document with a short survey of Bayesian statistics |
| Daphne's Approximate Group of Students (DAGS): Daphne Koller's research group on probabilistic representation, reasoning, and learning at Stanford University |
| Decision Systems Lab (DSL): Research group at the University of Pittsburgh with links to books and software on probabilistic, decision-theoretic, and econometric graphical models |
| LAPLACE Group - Bayesian Models for Perception, Inference and Action: Probabilistic reasoning and genetic algorithms for perception, inference and action: Bayesian cognitive and brain models, software for robotics, probabilistic inference engine |
| Learning Bayesian Networks from Data: 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 |
| Qualitative Verbal Explanations in Bayesian Belief Networks: Paper about combining probabilistic models and human-intuitive approaches to modeling uncertainty by generating qualitative verbal explanations of reasoning. |
| Query DAGs: A Practical Paradigm for Implementing Belief-Network Inference: 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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