Computers, Artificial Intelligence, Belief Networks
See Also:
Editor's Picks:
 - Kevin Murphy's tutorial, including a recommended reading list.
- Main association for belief network researchers. Runs the annual Uncertainty in Artificial Intelligence (UAI) conferences, and the UAI mailing list.
- Paper about combining probabilistic models and human-intuitive approaches to modeling uncertainty by generating qualitative verbal explanations of reasoning.
- Maintained by Gal Elidan - over a dozen publicly available networks with documentation, in several popular interchange formats
- Briefing document with a short survey of Bayesian statistics
- A survey and tutorial by Daryle Niedermayer - covers material on Bayesian inference in general and selected industrial applications of graphical models
- Eugene Santos' lists of belief network research, papers, and systems.
- Software, publications, teaching material, and news on belief revision - from the Business and Technology Research Laboratory at the University of Newcastle, Australia
- Daphne Koller's research group on probabilistic representation, reasoning, and learning at Stanford University
- Dynamic Trees are mixtures of tree structured belief networks, and are used as models for image segmentation and tracking.
- Probabilistic reasoning and genetic algorithms for perception, inference and action: Bayesian cognitive and brain models, software for robotics, probabilistic inference engine
- 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.
- Research group at the University of Pittsburgh with links to books and software on probabilistic, decision-theoretic, and econometric graphical models
- 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
- A free, interactive tutorial on Bayesian modeling, in particular dependence and classification modeling.
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