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 uncertainty and missing data. Belief Networks Artificial Intelligence Computers.

- Past (Artificial Intelligence)

- Wikipedia: A Bayesian network, Bayes network, belief network, Bayes model or probabilistic directed acyclic graphical model is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases.
- Bayesian networks
- Graphical models

- A Brief Introduction to Graphical Models and Bayesian Networks
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Kevin Murphy's tutorial, Brief Introduction to Graphical Models and Bayesian Networks, including a recommended reading list. - A B-Course - Dependence and classification modeling
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A free, Dependence and classification modeling, interactive tutorial on Bayesian modeling, in particular dependence and classification modeling. B-Course - Dependence and classification modeling. - Briefing Cause, chance and Bayesian statistics
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Briefing document with a short survey of Bayesian statistics Cause, chance and Bayesian statistics. - Dynamic Belief Networks and Variational Methods : Amos Storkey
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Dynamic Trees are mixtures of tree structured belief networks, Networks and Variational Methods : Amos Storkey, and are used as models for image segmentation and tracking. Belief Networks and Variational Methods : Amos Storkey. - Paper Qualitative Verbal Explanations in Bayesian Belief Networks
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Paper about combining probabilistic models and human-intuitive approaches to modeling uncertainty by generating qualitative verbal explanations of reasoning. Qualitative Verbal Explanations in Bayesian Belief Networks. - Slides Learning Bayesian Networks from Data
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Slides and additional notes from a tutorial by Nir Friedman and Daphne Koller on automated learning of belief networks, Bayesian Networks from Data, given at the Neural Information Processing Systems (NIPS-2001) conference Learning Bayesian Networks from Data. - Main Association for Uncertainty in Artificial Intelligence
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Main association for belief network researchers. for Uncertainty in Artificial Intelligence. Runs the annual Uncertainty in Artificial Intelligence (UAI) conferences, and the UAI mailing list. Association for Uncertainty in Artificial Intelligence. - Software, Belief Revision
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Software, Revision, publications, teaching material, and news on belief revision - from the Business and Technology Research Laboratory at the University of Newcastle, Australia Belief Revision. - Research Decision Systems Lab (DSL)
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Research group at the University of Pittsburgh with links to books and software on probabilistic, Systems Lab (DSL), decision-theoretic, and econometric graphical models Decision Systems Lab (DSL). - Article Query DAGs: A Practical Paradigm for Implementing Belief-Network Inference
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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. Query DAGs: A Practical Paradigm for Implementing Belief-Network Inference. - Daphne Daphne's Approximate Group of Students (DAGS)
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Daphne Koller's research group on probabilistic representation, Approximate Group of Students (DAGS), reasoning, and learning at Stanford University Daphne's Approximate Group of Students (DAGS). - May Special Track on Uncertainty, FLAIRS 2001
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May 21-23 2001. Track on Uncertainty, FLAIRS 2001. Key West, Florida. FLAIRS seeks to bring together researchers working on issues related to reasoning under uncertainty. Special Track on Uncertainty, FLAIRS 2001. - Main Uncertainty in Artificial Intelligence (UAI)
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Main conference for Belief Networks. Artificial Intelligence (UAI). Uncertainty in Artificial Intelligence (UAI). - Introduction Principal Curves Page
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Introduction to principal curves, Curves Page, with summary of and links to publications, demo, and software. Principal Curves Page. - A Reinforcement Learning Repository
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A centralized resource for researchers of reinforcement learning. Learning Repository. Maintained at University of Massachusetts, Amherst. Reinforcement Learning Repository. - Developing BAT (Bayesian Automated Taxi) project
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Developing next generation real-time decision making tools for vision-guided automated intelligent cars. (Bayesian Automated Taxi) project. BAT (Bayesian Automated Taxi) project. - Designed Heart Disease Program
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Designed to act as an intellectual sounding board, Disease Program, assisting diagnosis and anticipating the effects of therapy for cardiovascular disorders. Heart Disease Program. - A SamIam: Sensitivity Analysis, Modeling, Inference and More
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A tool for modeling and reasoning with Bayesian networks, Sensitivity Analysis, Modeling, Inference and More, developed in Java by the Automated Reasoning Group of Professor Adnan Darwiche at UCLA. SamIam: Sensitivity Analysis, Modeling, Inference and More. - Tool Pulcinella
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Tool for propagating uncertainty through local computations based on the general framework of valuation systems proposed by Shenoy and Shafer. Pulcinella. - Introductory AI Topics
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Introductory resources concerning artificial intelligence and its many dimensions. AI Topics. - A Neuron AI Directory: Artificial Intelligence resources
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A general AI directory. Neuron AI Directory: Artificial Intelligence resources. - Alexa: Belief Networks Artificial Intelligence

alexa:/topsites/category/Top/Computers/Artificial_Intelligence/Belief_Networks

Alexa Directory Top Sites: Belief Networks Artificial Intelligence - DMOZ: Belief Networks Artificial Intelligence

dmoz:/Computers/Artificial_Intelligence/Belief_Networks

dmoz.org Directory: Belief Networks Artificial Intelligence