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Briefing document with a short survey of Bayesian statistics
http://www.abelard.org/briefings/bayes.htm
Paper about combining probabilistic models and human-intuitive approaches to modeling uncertainty by generating qualitative verbal explanations of reasoning.
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Qualitative Verbal Explanations in Bayesian Belief Networks Bayesian belief networks in systems that interact directly with human users, such as decision support systems, requires effective user interfaces. The principal task of such interfaces is bridging the gap between probabilistic models and human intuitive approaches to modeling uncertainty....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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Query DAGs: A Practical Paradigm for Implementing Belief-Network Inference We describe a new paradigm for implementing inference in belief networks, which consists of two steps 1) compiling a belief network into an arithmetic expression called a Query DAG (Q-DAG and (2) answering queries using a simple evaluation algorithm. Each...Dynamic Trees are mixtures of tree structured belief networks, and are used as models for image segmentation and tracking.
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Amos Storkey Research Belief Networks Viasul iulslin of thethe mnoth Hough Transform Amos Storkey Tutorial: Introduction to Belief Networks As an introduction to the research I have been doing using and developing belief network approaches, I thought it might be useful to provide a basic...Eugene Santos' lists of belief network research, papers, and systems.
http://excalibur.brc.uconn.edu/~baynet/
Kevin Murphy's tutorial, including a recommended reading list.
http://www.cs.berkeley.edu/~murphyk/Bayes/bayes.html
A free, interactive tutorial on Bayesian modeling, in particular dependence and classification modeling.
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B-Course It can also be used as an interactive tutorial which provides you with data sets that have been prepared in advance. B-Course can be used as an analysis tool for any research where dependence or classification modeling based on 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
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NIPS 2001 Tutorial: Learning Bayesian Networks From Data These are the presentation materials from a 2 hour tutorial given at NIPS 2001. Powerpoint Presentation Show (3.25MB Online Presentation. Printout, 6 slids per page, no animation Postscript (7.12MB Compressed Postscript (1.66MB PDF (1.72MB Bibliography and Additional Readings Postscript, PDF....Software, publications, teaching material, and news on belief revision - from the Business and Technology Research Laboratory at the University of Newcastle, Australia
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Welcome to Belief Revision! We all know that beliefs can sometimes be wrong, so intelligent agents need to be able to revise beliefs when they acquire new information that contradicts their old beliefs. Belief Revision capabilities are crucially important for sound decision making and...Daphne Koller's research group on probabilistic representation, reasoning, and learning at Stanford University
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DAGS Daphne Koller's Research Group Our main research focus is on dealing with complex domains that involve large amounts of uncertainty. Our work builds on the framework of probability theory, decision theory, and game theory, but uses techniques from artificial intelligence and computer science to...Maintained by Gal Elidan - over a dozen publicly available networks with documentation, in several popular interchange formats
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Bayesian Network Repository Our in intention is to construct a repository that will allow us empirical research within our community by facilitating (1)better reproducibility of results, and (2) better comparisons among competing approach. Both of these are required to measure progress on problems...A survey and tutorial by Daryle Niedermayer - covers material on Bayesian inference in general and selected industrial applications of graphical models
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An Introduction to Bayesian Networks and their Contemporary Applications Networks are becoming an increasingly important area for research and application in the entire field of Artificial Intelligence. This paper explores the nature and implications for Bayesian Networks beginning with an overview and comparison of inferential statistics and Bayes Theorem....Research group at the University of Pittsburgh with links to books and software on probabilistic, decision-theoretic, and econometric graphical models
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GeNIe SMILE We have moved the site to the following location: http dsl.sis.pitt.edu (you will be automatically redirected in 3 seconds)...Probabilistic reasoning and genetic algorithms for perception, inference and action: Bayesian cognitive and brain models, software for robotics, probabilistic inference engine
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Bayesian programming Bayesian networks cognitive robotics S interdisciplinary project (in french) Bayesian models for motion generation CNRS interdisciplinary project (in french) ProBayes start-up, Mastering Uncertainty Probability theory is nothing but common sense reduced to calculation Pierre-Simon de Laplace All the illuminations on this site come from...