See also:
- 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.
- Bayesian Network Repository - Maintained by Gal Elidan - over a dozen publicly available networks with documentation, in several popular interchange formats
- 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.
- Belief Revision - 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
- An Introduction to Bayesian Networks and Their Contemporary Applications - A survey and tutorial by Daryle Niedermayer - covers material on Bayesian inference in general and selected industrial applications of graphical models
- LAPLACE Group - Bayesian Models for Perception, Inference and Action - Probabilistic reasoning and genetic algorithms for perception, inference and action: Bayesian cognitive and brain models, software for robotics, probabilistic inference engine
- 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.
/Computers/Artificial_Intelligence/Belief_Networks/
|
|
