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A Brief Introduction to Graphical Models and Bayesian Networks
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
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
Main association for belief network researchers. Runs the annual Uncertainty in Artificial Intelligence (UAI) conferences, and the UAI mailing list.
http://www.auai.org/
B-Course - Dependence and classification modeling
A free, interactive tutorial on Bayesian modeling, in particular dependence and classification modeling.
http://b-course.cs.helsinki.fi
Bayesian Network Repository
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/
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.
http://www.anc.ed.ac.uk/~amos/belief.html
Belief Revision
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
Briefing document with a short survey of Bayesian statistics
http://www.abelard.org/briefings/bayes.htm
Daphne's Approximate Group of Students (DAGS)
Daphne Koller's research group on probabilistic representation, reasoning, and learning at Stanford University
http://dags.stanford.edu
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
http://www.sis.pitt.edu/~dsl/
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
http://www-laplace.imag.fr
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
http://www.cs.huji.ac.il/~nirf/Nips01-Tutorial/
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.
http://www.pitt.edu/~druzdzel/abstracts/aisb.html
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.
http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume6/darwiche97a-html/jair-f.html
UConn list of Bayesian Network Resources
Eugene Santos' lists of belief network research, papers, and systems.
http://excalibur.brc.uconn.edu/~baynet/
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