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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- 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.
- 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.
- 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.
- A Brief Introduction to Graphical Models and Bayesian Networks Kevin Murphy's tutorial, including a recommended reading list.
- Cause, chance and Bayesian statistics Briefing document with a short survey of Bayesian statistics
- 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
- Daphne's Approximate Group of Students (DAGS) Daphne Koller's research group on probabilistic representation, reasoning, and learning at Stanford University
- 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.