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Advances in probabilistic graphical models

Author: Peter Lucas; José A Gámez; Antonio Salmerón
Publisher: Berlin ; New York : Springer, ©2007.
Series: Studies in fuzziness and soft computing, v. 213
Edition/Format:   Print book : EnglishView all editions and formats
Summary:

This book brings together important topics of current research in probabilistic graphical modeling, learning from data and probabilistic inference.

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Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Peter Lucas; José A Gámez; Antonio Salmerón
ISBN: 9783540689942 354068994X
OCLC Number: 144763968
Description: x, 306 pages : illustrations (some color) ; 24 cm.
Contents: Foundations.- Markov Equivalence in Bayesian Networks.- A Causal Algebra for Dynamic Flow Networks.- Graphical and Algebraic Representatives of Conditional Independence Models.- Bayesian Network Models with Discrete and Continuous Variables.- Sensitivity Analysis of Probabilistic Networks.- Inference.- A Review on Distinct Methods and Approaches to Perform Triangulation for Bayesian Networks.- Decisiveness in Loopy Propagation.- Lazy Inference in Multiply Sectioned Bayesian Networks Using Linked Junction Forests.- Learning.- A Study on the Evolution of Bayesian Network Graph Structures.- Learning Bayesian Networks with an Approximated MDL Score.- Learning of Latent Class Models by Splitting and Merging Components.- Decision Processes.- An Efficient Exhaustive Anytime Sampling Algorithm for Influence Diagrams.- Multi-currency Influence Diagrams.- Parallel Markov Decision Processes.- Applications.- Applications of HUGIN to Diagnosis and Control of Autonomous Vehicles.- Biomedical Applications of Bayesian Networks.- Learning and Validating Bayesian Network Models of Gene Networks.- The Role of Background Knowledge in Bayesian Classification.
Series Title: Studies in fuzziness and soft computing, v. 213
Responsibility: Peter Lucas, José A. Gámez, Antonio Salmerón, eds.
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