NUST Institutions Library Catalogue

Modeling and reasoning with Bayesian networks Adnan Darwiche.

By: Darwiche, Adnan, 1966-Material type: TextTextPublisher: Cambridge ; New York : Cambridge University Press, 2009Description: xii, 548 p. : ill. ; 26 cmISBN: 9780521884389 ; 0521884381 (hardback)Subject(s): Bayesian statistical decision theory -- ME, MIS | Inference | Probabilities | ModelingDDC classification: 519.542 DAR LOC classification: QA279.5 | .D37 2009Online resources: Table of contents only
Contents:
Introduction -- Propositional logic -- Probability calculus -- Bayesian networks -- Building Bayesian networks -- Inference by variable elimination -- Inference by factor elimination -- Inference by conditioning -- Models for graph decomposition -- Most likely instantiations -- The complexity of probabilistic inference -- Compiling Bayesian networks -- Inference with local structure -- Approximate inference by belief propagation -- Approximate inference by stochastic sampling -- Sensitivity analysis -- Learning : the maximum likelihood approach -- Learning : the Bayesian approach.
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Item type Current location Home library Call number Status Date due Barcode Item holds
Book Book Pakistan Navy Engineering College (PNEC)
Pakistan Navy Engineering College (PNEC)
519.542 DAR (Browse shelf) Available PNECLIB-030467
Total holds: 0

Includes Index http://www.amazon.com/Modeling-Reasoning-Bayesian-Networks-Darwiche/dp/0521884381/ref=sr_1_1?s=books&ie=UTF8&qid=1409386482&sr=1-1&keywords=9780521884389

Includes bibliographical references (p. 527-539) and index.

Introduction -- Propositional logic -- Probability calculus -- Bayesian networks -- Building Bayesian networks -- Inference by variable elimination -- Inference by factor elimination -- Inference by conditioning -- Models for graph decomposition -- Most likely instantiations -- The complexity of probabilistic inference -- Compiling Bayesian networks -- Inference with local structure -- Approximate inference by belief propagation -- Approximate inference by stochastic sampling -- Sensitivity analysis -- Learning : the maximum likelihood approach -- Learning : the Bayesian approach.

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