Belief Networks (Artificial Intelligence)

Belief Networks further on these related pages:

Conferences and Events Projects and Systems Software

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Belief Networks Sites:

Cause, chance and Bayesian statistics Cause, chance and Bayesian statistics: Briefing document with a short survey of Bayesian statistics (Belief Networks) http://www.abelard.org/briefings/bayes.htm B-Course - Dependence and classification modeling B-Course - Dependence and classification modeling: A free, interactive tutorial on Bayesian modeling, in particular dependence and classification modeling. (Belief Networks) http://b-course.cs.helsinki.fi Association for Uncertainty in Artificial Intelligence 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. (Belief Networks) http://www.auai.org/ Decision Systems Lab (DSL) 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 (Belief Networks) http://www.sis.pitt.edu/~dsl/ LAPLACE Group - Bayesian Models for Perception, Inference and Action 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 (Belief Networks) http://www-laplace.imag.fr UConn list of Bayesian Network Resources UConn list of Bayesian Network Resources: Eugene Santos' lists of belief network research, papers, and systems. (Belief Networks) http://excalibur.brc.uconn.edu/~baynet/ Belief Networks and Variational Methods : Amos Storkey 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 Networks) http://www.anc.ed.ac.uk/~amos/belief.html Belief Revision Belief Revision: Software, publications, teaching material, and news on belief revision - from the Business and Technology Research Laboratory at the University of Newcastle, Australia (Belief Networks) http://beliefrevision.org Bayesian Network Repository Bayesian Network Repository: Maintained by Gal Elidan - over a dozen publicly available networks with documentation, in several popular interchange formats (Belief Networks) http://www.cs.huji.ac.il/labs/compbio/Repository/ An Introduction to Bayesian Networks and Their Contemporary Applications 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 (Belief Networks) http://www.niedermayer.ca/papers/bayesian/ Daphne's Approximate Group of Students (DAGS) Daphne's Approximate Group of Students (DAGS): Daphne Koller's research group on probabilistic representation, reasoning, and learning at Stanford University (Belief Networks) http://dags.stanford.edu Qualitative Verbal Explanations in Bayesian Belief Networks 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. (Belief Networks) http://www.pitt.edu/~druzdzel/abstracts/aisb.html A Brief Introduction to Graphical Models and Bayesian Networks A Brief Introduction to Graphical Models and Bayesian Networks: Kevin Murphy's tutorial, including a recommended reading list. (Belief Networks) http://www.cs.berkeley.edu/~murphyk/Bayes/bayes.html Learning Bayesian Networks from Data 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 (Belief Networks) http://www.cs.huji.ac.il/~nirf/Nips01-Tutorial/ Query DAGs: A Practical Paradigm for Implementing Belief-Network Inference 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. (Belief Networks) http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume6/darwiche97a-html/jair-f.html