Optimized Bayesian Dynamic Advising: Theory and Algorithms (Advanced Information and Knowledge Processing)

Karny, Miroslav [Editor]

ISBN 10: 1852339284 / ISBN 13: 9781852339289

Verlag: Springer, 2006

Beschreibung

Springer Verlag; London, 2006. Hardcover. A Near Fine, binding firm, interior and extremities tidy, book appears unread, trace handling marks, mild crimping bottom spine edge, small and faint smudge mark on fore-edge, CD-ROM included in un-opened plastic pocket affirmed to rear pastedown, scratch mark mid rear board, without Dust wrapper. A nice, clean and unmarked copy. 8vo[octavo or approx. 6 x 9 inches], 529pp., references, indexed. We pack securely and ship daily with delivery confirmation on every book. The picture on the listing page is of the actual book for sale. Additional Scan(s) are available for any item, please inquire.Please note: Oversized books/sets MAY require additional postage then what is quoted for 2.2lb book.

Bestandsnummer des Verkäufers SKU1037211

Bibliografische Details

• Titel: Optimized Bayesian Dynamic Advising: Theory and Algorithms (Advanced Information and Knowledge Processing)
• Verlag: Springer
• Erscheinungsjahr: 2006
• Sprache: Englisch
• ISBN-10: 1852339284
• ISBN-13: 9781852339289
• Einband: hardcover
• Zustand: As New
• Verkäuferkataloge:
  • Math Text Books

Über diesen Titel

Inhaltsangabe

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.

Von der hinteren Coverseite

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.