Optimized bayesian dynamic advising : theory and algorithms
Author(s)
Bibliographic Information
Optimized bayesian dynamic advising : theory and algorithms
（Advanced information and knowledge processing）
Springer, c2006
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Note
Includes bibliographical references (p. [511]-521) and index
Description and Table of Contents
Description
A state-of-the-art research monograph providing consistent treatment of supervisory control, by one of the world's leading groups in the area of Bayesian identification, control, and decision making.
Table of Contents
Underlying theory.- Approximate and feasible learning.- Approximate design.- Problem formulation.- Solution and principles of its approximation: learning part.- Solution and principles of its approximation: design part.- 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.- Concluding remarks.
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