|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.
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.