Studies in Fuzziness and Soft Computing

By: Peter Lucas (Editor), JosÃ© A. GÃ¡mez (Editor), Antonio SalmerÃ³n Cerdan (Editor)

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Hardcover | 5 February 2007

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Hardcover
396 Pages

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23.5 x 15.88 x 1.91

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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.

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You Can Find This Book In

Non-Fiction Mathematics Applied Mathematics Stochastics Computing & I.T.Computer Science Artificial Intelligence Neural Networks & Fuzzy Systems Probability & Statistics Mathematical Modelling Combinatorics & Graph Theory Engineering & Technology

Technology in General Engineering in General

Markov equivalence in Bayesian networks	p. 3
A causal algebra for dynamic flow networks	p. 39
Graphical and algebraic representatives of conditional independence models	p. 55
Bayesian network models with discrete and continuous variables	p. 81
Sensitivity analysis of probabilistic networks	p. 103
A review on distinct methods and approaches to perform triangulation for Bayesian networks	p. 127
Decisiveness in loopy propagation	p. 153
Lazy inference in multiply sectioned Bayesian networks using linked junction forests	p. 175
A study on the evolution of Bayesian network graph structures	p. 193
Learning Bayesian networks with an approximated MDL score	p. 215
Learning of latent class models by splitting and merging components	p. 235
An efficient exhaustive anytime sampling algorithm for influence diagrams	p. 255
Multi-currency influence diagrams	p. 275
Parallel Markov decision processes	p. 295
Applications of HUGIN to diagnosis and control of autonomous vehicles	p. 313
Biomedical applications of Bayesian networks	p. 333
Learning and validating Bayesian network models of gene networks	p. 359
The role of background knowledge in Bayesian classification	p. 377
Table of Contents provided by Blackwell. All Rights Reserved.

Studies in Fuzziness and Soft Computing

At a Glance

Hardcover

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