Belief Networks Artificial Intelligence

Bayesian networks are used to show and calculate the effects of pieces of knowledge on each other. They are strongly related to expert systems, but use probability theory to calculate those effects and can therefore easily deal with problems like uncertainty and missing data.

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

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