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A Brief Introduction to Graphical Models and Bayesian Networks open in new window
Kevin Murphy's tutorial, including a recommended reading list.
(http://www.cs.berkeley.edu/~murphyk/Bayes/bayes.html)

An Introduction to Bayesian Networks and Their Contemporary Applications open in new window
A survey and tutorial by Daryle Niedermayer - covers material on Bayesian inference in general and selected industrial applications of graphical models
(http://www.niedermayer.ca/papers/bayesian/)

Association for Uncertainty in Artificial Intelligence open in new window
Main association for belief network researchers. Runs the annual Uncertainty in Artificial Intelligence (UAI) conferences, and the UAI mailing list.
(http://www.auai.org/)

Bayesian Network Repository open in new window
Maintained by Gal Elidan - over a dozen publicly available networks with documentation, in several popular interchange formats
(http://www.cs.huji.ac.il/labs/compbio/Repository/)

B-Course - Dependence and classification modeling open in new window
A free, interactive tutorial on Bayesian modeling, in particular dependence and classification modeling.
(http://b-course.cs.helsinki.fi)

Belief Networks and Variational Methods : Amos Storkey open in new window
Dynamic Trees are mixtures of tree structured belief networks, and are used as models for image segmentation and tracking.
(http://www.anc.ed.ac.uk/~amos/belief.html)

Belief Revision open in new window
Software, publications, teaching material, and news on belief revision - from the Business and Technology Research Laboratory at the University of Newcastle, Australia
(http://beliefrevision.org)

Cause, chance and Bayesian statistics open in new window
Briefing document with a short survey of Bayesian statistics
(http://www.abelard.org/briefings/bayes.htm)

Daphne's Approximate Group of Students (DAGS) open in new window
Daphne Koller's research group on probabilistic representation, reasoning, and learning at Stanford University
(http://dags.stanford.edu)

Decision Systems Lab (DSL) open in new window
Research group at the University of Pittsburgh with links to books and software on probabilistic, decision-theoretic, and econometric graphical models
(http://shanghai.exp.sis.pitt.edu)

Learning Bayesian Networks from Data open in new window
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
(http://www.cs.huji.ac.il/~nirf/Nips01-Tutorial/)

Qualitative Verbal Explanations in Bayesian Belief Networks open in new window
Paper about combining probabilistic models and human-intuitive approaches to modeling uncertainty by generating qualitative verbal explanations of reasoning.
(http://www.pitt.edu/~druzdzel/abstracts/aisb.html)

Query DAGs: A Practical Paradigm for Implementing Belief-Network Inference open in new window
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.
(http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume6/darwic)

UConn list of Bayesian Network Resources open in new window
Eugene Santos' lists of belief network research, papers, and systems.
(http://excalibur.brc.uconn.edu/~baynet/)


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