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 www
Kevin Murphy's tutorial, Brief Introduction to Graphical Models and Bayesian Networks, including a recommended reading list.
- Research Decision Systems Lab (DSL) www
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).
- Software, Belief Revision www
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.
- Article Query DAGs: A Practical Paradigm for Implementing Belief-Network Inference www
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.
- Dynamic Belief Networks and Variational Methods : Amos Storkey www
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.
- Slides Learning Bayesian Networks from Data www
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.
- Briefing Cause, chance and Bayesian statistics www
Briefing document with a short survey of Bayesian statistics Cause, chance and Bayesian statistics.
- Daphne Daphne's Approximate Group of Students (DAGS) www
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).
- A B-Course - Dependence and classification modeling www
A free, Dependence and classification modeling, interactive tutorial on Bayesian modeling, in particular dependence and classification modeling. B-Course - Dependence and classification modeling.
- Main Association for Uncertainty in Artificial Intelligence www
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.
- Paper Qualitative Verbal Explanations in Bayesian Belief Networks www
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.
- May Special Track on Uncertainty, FLAIRS 2001 www
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) www
Main conference for Belief Networks. Artificial Intelligence (UAI). Uncertainty in Artificial Intelligence (UAI).
- Collection Neural Network Centers Around the World www
Collection of Neural Network resources sorted by topic. Neural Network Centers Around the World.
- A Neuroinformatics www
A hub for the neuroinformatics community, Neuroinformatics, with information on workshops and courses. Also hosts the comp-neuro mailing list. Neuroinformatics.
- Decision SERENE: Safety and Risk Evaluation using Bayesian Nets www
Decision support method and tool for quantifying the safety of complex systems. SERENE: Safety and Risk Evaluation using Bayesian Nets.
- Using DINA (Danish Information Network in the Agricultural Sciences) www
Using belief networks for crop prediction. (Danish Information Network in the Agricultural Sciences). DINA (Danish Information Network in the Agricultural Sciences).
- A IBAyes www
A probabilistic reasoning tool that allows its user to model uncertain situations and to perform inference using Bayesian networks and its variants such as Influence Nets. IBAyes. The tool is developed by the Artificial Intelligence Lab @ IBA and currently works in Windows environment. IBAyes.
- Robert RISO www
Robert Dodier's open source package for distributed, RISO, heterogeneous belief networks in Java - allows different conditional distributions RISO.
- SIGART SIGART Electronic Information Services www
SIGART is the ACM Special Interest Group on Artificial Intelligence. SIGART Electronic Information Services.
- Performance Processor Performance www
Performance and evaluation of processors, Performance, assembly programming, and unlimited shareware. Processor Performance.
- Alexa: Belief Networks Artificial Intelligence
Alexa Directory Top Sites: Belief Networks Artificial Intelligence
- DMOZ: Belief Networks Artificial Intelligence
dmoz.org Directory: Belief Networks Artificial Intelligence