Optimized Bayesian Dynamic Advising

. Ed(s): Karny, Miroslav (Academy of Sciences of the Czech Republic, Prague, Czech Republic)

ISBN 10: 1852339284 / ISBN 13: 9781852339289

Editore: Springer London Ltd, 2005

Riguardo questo articolo

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. It is accompanied by a CD that contains a specialized Matlab-based Mixtools toolbox, and examples illustrating the important areas. Editor(s): Karny, Miroslav (Academy of Sciences of the Czech Republic, Prague, Czech Republic). Series: Advanced Information and Knowledge Processing. Num Pages: 546 pages, 6 black & white tables, biography. BIC Classification: PBU; UMB. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 234 x 156 x 30. Weight in Grams: 2070. . 2005. 2006. Hardback. . . . . Books ship from the US and Ireland.

Codice articolo V9781852339289

Dati bibliografici

• Titolo: Optimized Bayesian Dynamic Advising
• Editore: Springer London Ltd
• Data di pubblicazione: 2005
• Lingua: Inglese
• ISBN 10: 1852339284
• ISBN 13: 9781852339289
• Rilegatura: Rilegato
• Condizione: New

Descrizione articolo

Riassunto

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.

Dalla quarta di copertina

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.