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Open Directory - Computers: Artificial Intelligence: Belief Networks
Conferences and Events
Projects and Systems
Computers: Artificial Intelligence: Neural Networks
Science: Math: Probability
Science: Math: Statistics: Bayesian Analysis
A Brief Introduction to Graphical Models and Bayesian Networks
- Kevin Murphy's tutorial, including a recommended reading list.
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.
B-Course - Dependence and classification modeling
- A free, interactive tutorial on Bayesian modeling, in particular dependence and classification modeling.
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.
- Software, publications, teaching material, and news on belief revision - from the Business and Technology Research Laboratory at the University of Newcastle, Australia
Cause, chance and Bayesian statistics
- Briefing document with a short survey of Bayesian statistics
Daphne's Approximate Group of Students (DAGS)
- Daphne Koller's research group on probabilistic representation, reasoning, and learning at Stanford University
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
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
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.
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