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 uncertai...
This page was last updated on October 14th, 2008
This page was last updated on October 14th, 2008
Belief Networks (from Artificial Intelligence)
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Belief Revision
Software, publications, teaching material, and news on belief revision - from the Business and Technology Research Laboratory at the University of Newcastle, Australia
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.
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.
Cause, chance and Bayesian statistics
Briefing document with a short survey of Bayesian statistics
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
A Brief Introduction to Graphical Models and Bayesian Networks
Kevin Murphy's tutorial, including a recommended reading list.
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
B-Course - Dependence and classification modeling
A free, interactive tutorial on Bayesian modeling, in particular dependence and classification modeling.
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
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
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.
Bayesian Network Repository
Maintained by Gal Elidan - over a dozen publicly available networks with documentation, in several popular interchange formats
UConn list of Bayesian Network Resources
Eugene Santos' lists of belief network research, papers, and systems.
Daphne's Approximate Group of Students (DAGS)
Daphne Koller's research group on probabilistic representation, reasoning, and learning at Stanford University
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.
Software, publications, teaching material, and news on belief revision - from the Business and Technology Research Laboratory at the University of Newcastle, Australia
Dynamic Trees are mixtures of tree structured belief networks, and are used as models for image segmentation and tracking.
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.
Briefing document with a short survey of Bayesian statistics
A survey and tutorial by Daryle Niedermayer - covers material on Bayesian inference in general and selected industrial applications of graphical models
Kevin Murphy's tutorial, including a recommended reading list.
Probabilistic reasoning and genetic algorithms for perception, inference and action: Bayesian cognitive and brain models, software for robotics, probabilistic inference engine
A free, interactive tutorial on Bayesian modeling, in particular dependence and classification modeling.
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
Research group at the University of Pittsburgh with links to books and software on probabilistic, decision-theoretic, and econometric graphical models
Paper about combining probabilistic models and human-intuitive approaches to modeling uncertainty by generating qualitative verbal explanations of reasoning.
Maintained by Gal Elidan - over a dozen publicly available networks with documentation, in several popular interchange formats
Eugene Santos' lists of belief network research, papers, and systems.
Daphne Koller's research group on probabilistic representation, reasoning, and learning at Stanford University
Main association for belief network researchers. Runs the annual Uncertainty in Artificial Intelligence (UAI) conferences, and the UAI mailing list.
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