A Brief Introduction to Graphical Models and Bayesian Networks
Kevin Murphy's tutorial, including a recommended reading list. |
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 |
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. |
Bayesian Network Repository
Maintained by Gal Elidan - over a dozen publicly available networks with documentation, in several popular interchange formats |
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 |
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. |
Arts |
Business |
Computers |
Games |
Health |
Home |
News |
Recreation |
Reference
|
|
