| Association for Uncertainty in Artificial Intelligence |
| Assoc for Uncertainty in AI |
| http://www.auai.org/ |
| Main association for belief network researchers. Runs the annual Uncertainty in Artificial Intellige nce (UAI) conferences, and the UAI mailing list. |
| Web site for the Association for Uncertainty in Artificial Intelligence |
| AI, Artificial Intelligence, Bayesian, uncertainty and intelligent systems, uncertainty, probabilist ic inference, decision making under uncertainty |
| uncertainty |
| (SLD : auai.org) |
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| Qualitative Verbal Explanations in Bayesian Belief Networks |
| Qualitative Verbal Explanations in Bayesian Belief Networks |
| http://www.pitt.edu/~druzdzel/abstracts/aisb.html |
| Paper about combining probabilistic models and human-intuitive approaches to modeling uncertainty by generating qualitative verbal explanations of reasoning. |
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| bayesian, belief, networks, systems, qualitative, explanations, interfaces, probabilistic, verbal, e xplanations, networks, qualitative, belief, interactions, variables, present, network, explaining, t echniques, structure, technique, simple, reasoning, publications, update, formats, explanation, keyw ords, available, postscript, generating, generation, information, science, decision, effective, requ ires, support, directly, interact, abstract, application, program, intelligent, systems, department, uncertainty, modeling, approaches, intuitive, author |
pitt.edu - rank der domain 13247 (4921 in US)
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| Query DAGs: A Practical Paradigm for Implementing Belief-Network Inference |
| Query DAGs: A Practical Paradigm for Implementing Belief-Network Inference |
| http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume6/darwiche97a-html/jair-f.html |
| Article published in JAIR (Journal of AI Research) about a way to implement belief networks by compi ling networks into arithmetic expressions and then answering queries using an evaluation algorithm. |
| Query DAGs: A Practical Paradigm for Implementing Belief-Network Inference |
| jair-f |
| inference, belief, algorithm, evaluation, network, network, generation, networks, complexity, implem enting, represents, belief, inference, clustering, generating, simple, techniques, expression, algor ithm, arithmetic, example, hardware, algorithms, beirut, introduction, software, reducing, darwiche, standard, provan, paradigm, practical, generated, amounts, linear, interestingly, simplicity, frame work, utilize, proposed, applications, required, resources, intended, different, development, facili tates, platforms, relatively, computation, caching |
cmu.edu - rank der domain 6044 (2272 in US)
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| Computers/Artificial_Intelligence/Belief_Networks |
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| Computers/Artificial_Intelligence/Belief_Networks |
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| A Brief Introduction to Graphical Models and Bayesian Networks |
| Graphical Models |
| http://www.cs.berkeley.edu/~murphyk/Bayes/bayes.html |
| Kevin Murphy's tutorial, including a recommended reading list. |
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| models, hidden, graphical, bayesian, parents, structure, variables, possible, parameters, directed, called, probability, between, networks, common, inference, learning, methods, models, markov, indepe ndence, observed, networks, graphical, network, independent, example, theory, theory, because, appro ach, undirected, likelihood, systems, random, compute, system, number, problem, conditional, discret e, tutorial, values, inference, utility, algorithm, variable, kalman, lattice, represent, distributi ons |
berkeley.edu - rank der domain 1915 (777 in US)
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| B-Course - Dependence and classification modeling |
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| http://b-course.cs.helsinki.fi |
| A free, interactive tutorial on Bayesian modeling, in particular dependence and classification model ing. |
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helsinki.fi - rank der domain 18831 (35 in FI)
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| Cause, chance and Bayesian statistics |
| cause, chance and Bayesian statistics - Bayes theory for conditional and marginal probabilities |
| http://www.abelard.org/briefings/bayes.htm |
| Briefing document with a short survey of Bayesian statistics |
| briefing document to facilitate understanding Bayesian statistics. The statistical theory developed by Thomas Bayes enables analysis of conditional and marginal probabilities. Bayesian statistics enab les logical inference |
| Bayes,Bayesian probability,Bayesian theory,Bayesian logic,Bayes statistics,Bayes probability,Bayes l ogic,Bayesean,theorem,false positives,spam filter,false negatives,statistical inference,prior,subjec tivity,estimation,induction,distribution, |
| bayesian, probability, statistics, distribution, chance, methods, witness, statistical, theory, empi ric, approach, states, nature, sample, subjectivity, distributions, different, subjective, consider, probabilities, company, earlier, derived, reasoning, accident, conditions, question, introduction, documents, process, decisions, iterative, learning, common, computer, feedback, crowding, similar, p resented, experience, better, establishing, programmes, repeatedly, random, misidentifies, informati on, combined, combining, theorem, provides |
abelard.org - rank der domain 327893 (131330 in US)
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| Learning Bayesian Networks from Data |
| NIPS 2001 Tutorial: Learning Bayesian Networks From Data |
| http://www.cs.huji.ac.il/~nirf/Nips01-Tutorial/ |
| 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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| postscript, presentation, bayesian, networks, learning, tutorial, animation, compressed, readings, a dditional, bibliography, online, presentation, materials, friedman, daphne, koller, powerpoint, tuto rial, printout |
| (SLD : ac.il) |
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| Belief Revision |
| Welcome to Belief Revision! |
| http://beliefrevision.org |
| Software, publications, teaching material, and news on belief revision - from the Business and Techn ology Research Laboratory at the University of Newcastle, Australia |
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| beliefs, belief, revision, intelligent, agents, resources, pointers, choose, modified, forgotten, op tion, yourpassword, username, password, contact, laboratory, research, technology, beliefrevision, i nnovation, software, tutorials, conferences, people, publications, useful, newsletter, studio, creat ed, sometimes, revise, robots, infobots, intelligent, welcome, manage, design, achieve, acquire, rev ision, fundamental, belief, designed, website, communication, effective, capabilities, contradicts, information, crucially, important |
| (SLD : beliefrevision.org) |
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| Computers/Artificial_Intelligence/Belief_Networks |
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| Computers/Artificial_Intelligence/Belief_Networks |
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| Daphne's Approximate Group of Students (DAGS) |
| DAGS - Daphne Koller's Research Group |
| http://dags.stanford.edu |
| Daphne Koller's research group on probabilistic representation, reasoning, and learning at Stanford University |
| DAGS - Daphne Koller's Research Group working on Probabilistic Reasoning with Bayesian Networks, Mar kov Decision Processes and Probabilistic Relational Models. |
| research, probability, bayesain network, markov decision processes, probablilistic relational models |
| theory, decision, daphne, framework, complex, domains, research, koller, probabilistic, graphical, i nfluence, learning, networks, making, inference, diagrams, within, touches, processes, markov, repre sentation, encompass, copyright, approximate, students, reserved, rights, modeling, representational , language, bayesian, richer, extension, involve, amounts, uncertainty, probability, builds, dealing , people, research, projects, publications, professor, welcome, problems, models, artificial, techni ques, intelligence, computer |
stanford.edu - rank der domain 1213 (508 in US)
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| Bayesian Network Repository |
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| http://www.cs.huji.ac.il/labs/compbio/Repository/ |
| Maintained by Gal Elidan - over a dozen publicly available networks with documentation, in several p opular interchange formats |
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| (SLD : ac.il) |
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| An Introduction to Bayesian Networks and Their Contemporary Applications |
| An Introduction to Bayesian Networks and their Contemporary Applications |
| http://www.niedermayer.ca/papers/bayesian/ |
| A survey and tutorial by Daryle Niedermayer - covers material on Bayesian inference in general and s elected industrial applications of graphical models |
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| bayesian, probability, networks, network, contents, probabilities, variables, sample, marbles, condi tional, introduction, networks, example, samples, probability, information, inference, problem, inde pendent, theorem, current, network, autoclass, distribution, results, number, population, system, to morrow, parameters, burglary, market, useful, discovery, previously, confidence, process, saturation , decision, theory, events, relationships, earthquake, investor, knowledge, resulting, support, solu tion, potential, research, determine |
| (SLD : niedermayer.ca) |
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| Decision Systems Lab (DSL) |
| GeNIe & SMILE |
| http://www.sis.pitt.edu/~dsl/ |
| Research group at the University of Pittsburgh with links to books and software on probabilistic, de cision-theoretic, and econometric graphical models |
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| seconds, redirected, automatically, location, following |
pitt.edu - rank der domain 13247 (4921 in US)
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| Belief Networks and Variational Methods : Amos Storkey |
| Amos Storkey - Research - Belief Networks |
| http://homepages.inf.ed.ac.uk/amos/belief.html |
| Dynamic Trees are mixtures of tree structured belief networks, and are used as models for image segm entation and tracking. |
| Tutorial: Introduction to Belief Networks. A simple illustrated tutorial on belief networks (or Baye sian networks), with links and references for further reading. This tutorial focusses on the introdu ctory issues of design of Bayes nets, inference in belief nets, and learning belief network paramete rs. |
| Bayesian networks, belief networks, belief network, Bayes nets, Bayes networks, Bayesian belief net works, graphical models, tutorial, introduction, dynamic trees, variational, mean field, belief prop agation, belief nets, tutorial on belief networks, introduction to Bayes nets, learning, inference, examples, practical, probability models, Storkey ,Amos Storkey. |
| belief, network, probability, distribution, networks, probabilities, conditional, bayesian, variable s, posterior, inference, values, methods, belief, causal, example, graphical, approach, possible, ca lled, theory, networks, parameters, relationships, algorithm, whether, number, calculate, dependence , because, learning, statistics, information, direct, probabilistic, knowledge, simple, connected, i nference, between, variational, introduction, tutorial, certain, beliefs, another, messages, directe d, variable, general, particular |
| (SLD : ac.uk) |
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