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Learning in graphical models

Publisher: Cambridge, Mass. ; London : MIT Press, 1999.
Edition/Format:   Print book : EnglishView all editions and formats

Presents an exploration of issues related to learning within the graphical model formalism. This text covers topics such as: inference for Bayesian networks; Monte Carlo methods; variational methods;  Read more...


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Genre/Form: études diverses
Document Type: Book
ISBN: 0262600323 9780262600323
OCLC Number: 490659625
Description: 1 v. (VII-634 p.) ; 25 cm
Contents: Part 1 Inference: introduction to inference for Bayesian networks, Robert Cowell; advanced inference in Bayesian networks, Robert Cowell; inference in Bayesian networks using nested junction trees, Uffe Kjoerulff; bucket elimination - a unifying framework for probabilistic inference, R. Dechter; an introduction to variational methods for graphical models, Michael I. Jordan et al; improving the mean field approximation via the use of mixture distributions, Tommi S. Jaakkola and Michael I. Jordan; introduction to Monte Carlo methods, D.J.C. MacKay; suppressing random walls in Markov chain Monte Carlo using ordered overrelaxation, Radford M. Neal. Part 2 Independence: chain graphs and symmetric associations, Thomas S. Richardson; the multiinformation function as a tool for measuring stochastic dependence, M. Studeny and J. Vejnarova. Part 3 Foundations for learning: a tutorial on learning with Bayesian networks, David Heckerman; a view of the EM algorithm that justifies incremental, sparse and other variants, Radford M. Neal and Geoffrey E. Hinton. Part 4 Learning from data: latent variable models, Christopher M. Bishop; stochastic algorithms for exploratory data analysis - data clustering and data visualization, Joachim M. Buhmann; learning Bayesian networks with local structure, Nir Friedman and Moises Goldszmidt; asymptotic model selection for directed networks with hidden variables, Dan Geiger et al; a hierarchical community of experts, Geoffrey E. Hinton et al; an information-theoretic analysis of hard and soft assignment methods for clustering, Michael J. Kearns et al; learning hybrid Bayesian networks from data, Stefano Monti and Gregory F. Cooper; a mean field learning algorithm for unsupervised neural networks, Lawrence Saul and Michael Jordan; edge exclusion tests for graphical Gaussian models, Peter W.F. Smith and Joe Whittaker; hepatitis B - a case study in MCMC, D.J. Spiegelhalter et al; prediction with Gaussian processes - from linear regression to linear prediction and beyond, C.K.I. Williams.
Responsibility: ed. by Michael I. Jordan.


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"The state of the art presented by the experts in the field." Ross D. Shachter , Department of Engineering-Economic Systemsand Operations Research, Stanford University

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