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 Home » Artificial Intelligence » Belief Networks

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1 - 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/   [more pages from this URL]     2 - 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   [more pages from this URL]     3 - 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/   [more pages from this URL]     4 - 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   [more pages from this URL]     5 - Cause, chance and Bayesian statistics   Briefing document with a short survey of Bayesian statistics http://www.abelard.org/briefings/bayes.htm   [more pages from this URL]     6 - 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   [more pages from this URL]     7 - 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   [more pages from this URL]     8 - 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/   [more pages from this URL]     9 - 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   [more pages from this URL]     10 - 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   [more pages from this URL]

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