Advanced Information and Knowledge Processing

Theory and Algorithms

By: Miroslav Karny (Editor)

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Hardcover | 10 October 2005

At a Glance

Hardcover
548 Pages

Dimensions(cm)
24.77 x 16.51 x 3.18

Hardcover

$249.00

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Written by one of the world?s leading groups in the area of Bayesian identification, control, and decision making, this book provides the theoretical and algorithmic basis of optimized probabilistic advising.Starting from abstract ideas and formulations, and culminating in detailed algorithms, the book comprises a unified treatment of an important problem of the design of advisory systems supporting supervisors of complex processes. It introduces the theoretical and algorithmic basis of developed advising, relying on novel and powerful combination black-box modeling by dynamic mixture models and fully probabilistic dynamic optimization.Written for a broad audience, including developers of algorithms and application engineers, researchers, lecturers, and postgraduates, this book can be used as a reference tool, and an advanced text on Bayesian dynamic decision making. A CD contains a specialized Matlab-based Mixtools toolbox, and examples illustrating the most important and complex areas of the material presented.

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Introduction
Underlying Theory
Approximate and Feasible Learning
Approximate Design
Problem Formulation
Solution and Principles of its Approximation: Learning
Solution and Principles of its Approximation: Design
Learning with Normal Factors and Components
Design with Normal Mixtures
Learning with Markov Chain Factors and Components
Design with Markov Chain Mixtures
Sandwich BMTB for Mixture Initiation
Mixed Mixtures
Applications of the Advisory System
Conclusions
References
Index
Table of Contents provided by Publisher. All Rights Reserved.

Advanced Information and Knowledge Processing

Theory and Algorithms

At a Glance

Hardcover

Shipping

How to return your order

Defective items

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