Artificial Intelligence: Belief Networks

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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.