Bayesian Heuristic Approach to Discrete and Global Optimization - Algorithms, Visualization, Software, and Applications - Hardback - 1996
Jonas Mockus, William Eddy,Reklaitis, Gintaras (Purdue University, W. Lafayette, IN, USA)
Resumo
Bayesian decision theory is known to provide an effective framework for the practical solution of discrete and nonconvex optimization problems. This book covers various aspects ranging from formal presentation of Bayesian Approach, to its extension to the Bayesian Heuristic Strategy, and its utilization within the Dynamic Visualization strategy.
Year of publication: 1996
Pagination: 412 pages, biography
Format: Hardback
Serie: Nonconvex Optimization and Its Applications
Year of publication: 1996
Pagination: 412 pages, biography
Format: Hardback
Serie: Nonconvex Optimization and Its Applications
Bayesian Heuristic Approach to Discrete and Global...
Resumo
Bayesian decision theory is known to provide an effective framework for the practical solution of discrete and nonconvex optimization problems. This book covers various aspects ranging from formal presentation of Bayesian Approach, to its extension to the Bayesian Heuristic Strategy, and its utilization within the Dynamic Visualization strategy.
Year of publication: 1996
Pagination: 412 pages, biography
Format: Hardback
Serie: Nonconvex Optimization and Its Applications
Year of publication: 1996
Pagination: 412 pages, biography
Format: Hardback
Serie: Nonconvex Optimization and Its Applications
Publicidade
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Bayesian Heuristic Approach to Discrete and Global Optimization - Algorithms, Visualization, Software, and Applications - Hardback - 1996
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Características
- Editora
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Kluwer Academic Publishers
- Dimensão
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234 x 156 x 23
- Peso
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763
- Tema
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Optimization|Combinatorics & graph theory
- Origem
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United States
- EAN
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9780792343271
Publicidade
Publicidade