Optimized Bayesian Dynamic Advising

Miroslav Karny

ISBN 10: 1447156757 / ISBN 13: 9781447156758

Editore: Springer London Okt 2014, 2014

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This item is printed on demand - it takes 3-4 days longer - Neuware -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. 548 pp. Englisch.

Codice articolo 9781447156758

Dati bibliografici

• Titolo: Optimized Bayesian Dynamic Advising
• Editore: Springer London Okt 2014
• Data di pubblicazione: 2014
• Lingua: Inglese
• ISBN 10: 1447156757
• ISBN 13: 9781447156758
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 30 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 30 millimetri di profondità
• Peso: 820 grammi

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.