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Optimized Bayesian Dynamic ...
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, Optimized Bayesian Dynamic Advising 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. The proposed non-standard problem formulation and its solution mark a significant contribution to the design of anthropocentric automation systems. 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
Monografía
monografia Rebiun12016461 https://catalogo.rebiun.org/rebiun/record/Rebiun12016461 cr nn 008mamaa 100301s2006 xxk| s |||| 0|eng d 9781846282546 UPVA 996876782203706 UNAV 005.743 23 Böhm, Josef Optimized Bayesian Dynamic Advising Recurso electrónico] Theory and Algorithms by Josef Böhm, Tatiana V. Guy, Ladislav Jirsa, Ivan Nagy, Petr Nedoma, Ludvík Tesař ; edited by Miroslav Kárny London Springer London 2006 London London Springer London XV, 529p. 13 illus XV, 529p. 13 illus Advanced Information and Knowledge Processing Springer eBooks 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 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, Optimized Bayesian Dynamic Advising 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. The proposed non-standard problem formulation and its solution mark a significant contribution to the design of anthropocentric automation systems. 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 Forma de acceso: World Wide Web Guy, Tatiana V. Jirsa, Ladislav Nagy, Ivan Nedoma, Petr Tesař, Ludvík Kárný, Miroslav SpringerLink