Overview

In recent years considerable progress has been made in the area of probabilistic graphical models, in particular Bayesian networks and influence diagrams. Probabilistic graphical models have become mainstream in the area of uncertainty in artificial intelligence; contributions to the area are coming from computer science, mathematics, statistics and engineering. This carefully edited book brings together in one volume some of the most important topics of current research in probabilistic graphical modelling, learning from data and probabilistic inference. This includes topics such as the characterisation of conditional independence, the sensitivity of the underlying probability distribution of a Bayesian network to variation in its parameters, the learning of graphical models with latent variables and extensions to the influence diagram formalism. In addition, attention is given to important application fields of probabilistic graphical models, such as the control of vehicles, bioinformatics and medicine.

Full Product Details

Author:   Peter Lucas ,  José A. Gámez ,  Antonio Salmerón Cerdan
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition:   Softcover reprint of hardcover 1st ed. 2007
Volume:   213
Dimensions:   Width: 15.50cm , Height: 2.10cm , Length: 23.50cm
Weight:   0.623kg
ISBN:  

9783642088544

ISBN 10:   3642088546
Pages:   386
Publication Date:   19 November 2010
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Out of stock  
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Table of 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.

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