Directory of Belief Networks Resources

Home > Computers > Artificial Intelligence > Belief Networks

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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Resources in This Category

• 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.

• 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.

• 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.

• Cause, chance and Bayesian statistics

  http://www.abelard.org/briefings/bayes.htm
  Briefing document with a short survey of Bayesian statistics

• 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

• 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

• 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.

• 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.

Related Categories

• Home > Computers > Artificial Intelligence > Neural Networks
• Home > Science > Math > Probability
• Home > Science > Math > Statistics > Bayesian Analysis

 

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