Optimized Bayesian dynamic advising - Info and Reading Options
By Miroslav Karny
"Optimized Bayesian dynamic advising" was published by Springer in 2005 - London, it has 529 pages and the language of the book is English.
“Optimized Bayesian dynamic advising” Metadata:
- Title: ➤ Optimized Bayesian dynamic advising
- Author: Miroslav Karny
- Language: English
- Number of Pages: 529
- Publisher: Springer
- Publish Date: 2005
- Publish Location: London
“Optimized Bayesian dynamic advising” Subjects and Themes:
- Subjects: ➤ Bayesian statistical decision theory - Computer science - Artificial intelligence - Computer simulation - Optical pattern recognition - Mathematical statistics - Models and Principles - User Interfaces and Human Computer Interaction - Artificial Intelligence (incl. Robotics) - Simulation and Modeling - Pattern Recognition - Statistics and Computing/Statistics Programs
Edition Specifications:
- Pagination: xv, 529 p. ;
Edition Identifiers:
- The Open Library ID: OL22637103M - OL8533071W
- Online Computer Library Center (OCLC) ID: 61771780
- Library of Congress Control Number (LCCN): 2005923319
- ISBN-10: 1852339284
- All ISBNs: 1852339284
AI-generated Review of “Optimized Bayesian dynamic advising”:
"Optimized Bayesian dynamic advising" Description:
The Open Library:
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.
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