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	Association for Uncertainty in Artificial Intelligence - http://www.auai.org/ Main association for belief network researchers. Runs the annual Uncertainty in Artificial Intelligence (UAI) conferences, and the UAI mailing list.
	Daphne's Approximate Group of Students (DAGS) - http://dags.stanford.edu Daphne Koller's research group on probabilistic representation, reasoning, and learning at Stanford University
	A Brief Introduction to Graphical Models and Bayesian Networks - http://www.cs.berkeley.edu/~murphyk/Bayes/bayes.html Kevin Murphy's tutorial, including a recommended reading list.
	Belief Revision - http://beliefrevision.org Software, publications, teaching material, and news on belief revision - from the Business and Technology Research Laboratory at the University of Newcastle, Australia
	Decision Systems Lab (DSL) - http://www.sis.pitt.edu/~dsl/ Research group at the University of Pittsburgh with links to books and software on probabilistic, decision-theoretic, and econometric graphical models

	B-Course - Dependence and classification modeling - http://b-course.cs.helsinki.fi A free, interactive tutorial on Bayesian modeling, in particular dependence and classification modeling.
	Learning Bayesian Networks from Data - http://www.cs.huji.ac.il/~nirf/Nips01-Tutorial/ 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
	Cause, chance and Bayesian statistics - http://www.abelard.org/briefings/bayes.htm Briefing document with a short survey of Bayesian statistics
	Qualitative Verbal Explanations in Bayesian Belief Networks - http://www.pitt.edu/~druzdzel/abstracts/aisb.html Paper about combining probabilistic models and human-intuitive approaches to modeling uncertainty by generating qualitative verbal explanations of reasoning.
	An Introduction to Bayesian Networks and Their Contemporary Applications - http://www.niedermayer.ca/papers/bayesian/ A survey and tutorial by Daryle Niedermayer - covers material on Bayesian inference in general and selected industrial applications of graphical models

	Belief Networks and Variational Methods : Amos Storkey - http://homepages.inf.ed.ac.uk/amos/belief.html Dynamic Trees are mixtures of tree structured belief networks, and are used as models for image segmentation and tracking.
	Query DAGs: A Practical Paradigm for Implementing Belief-Network Inference - http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume6/darwiche97a-html/jair-f.html 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.
	Bayesian Network Repository - http://www.cs.huji.ac.il/labs/compbio/Repository/ Maintained by Gal Elidan - over a dozen publicly available networks with documentation, in several popular interchange formats

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