The cross-entropy (CE) method is one of the most significant developments in stochastic optimization and simulation in recent years. This book explains in detail how and why the CE method works. The CE method involves an iterative procedure where each iteration can be broken down into two phases: (a...

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Bibliographische Detailangaben

Hauptverfasser:	Rubinstein, Reuven Y. (VerfasserIn), Kroese, Dirk P. (VerfasserIn)
Format:	Elektronisch E-Book
Sprache:	English
Veröffentlicht:	New York, NY Springer New York 2004
Ausgabe:	1st ed. 2004
Schriftenreihe:	Information Science and Statistics
Schlagworte:	Simulation and Modeling Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Operations Research/Decision Theory Probability and Statistics in Computer Science Computational Intelligence Operations Research, Management Science Computer simulation Statistics  Operations research Decision making Mathematical statistics Computational intelligence Management science Stochastische Optimierung Monte-Carlo-Simulation Kombinatorische Optimierung Maschinelles Lernen
Online-Zugang:	UBY01 Volltext
Zusammenfassung:	The cross-entropy (CE) method is one of the most significant developments in stochastic optimization and simulation in recent years. This book explains in detail how and why the CE method works. The CE method involves an iterative procedure where each iteration can be broken down into two phases: (a) generate a random data sample (trajectories, vectors, etc.) according to a specified mechanism; (b) update the parameters of the random mechanism based on this data in order to produce a ''better'' sample in the next iteration. The simplicity and versatility of the method is illustrated via a diverse collection of optimization and estimation problems. The book is aimed at a broad audience of engineers, computer scientists, mathematicians, statisticians and in general anyone, theorist or practitioner, who is interested in fast simulation, including rare-event probability estimation, efficient combinatorial and continuous multi-extremal optimization, and machine learning algorithms. Reuven Y. Rubinstein is the Milford Bohm Professor of Management at the Faculty of Industrial Engineering and Management at the Technion (Israel Institute of Technology). His primary areas of interest are stochastic modelling, applied probability, and simulation. He has written over 100 articles and has published five books. He is the pioneer of the well-known score-function and cross-entropy methods. Dirk P. Kroese is an expert on the cross-entropy method. He has published close to 40 papers in a wide range of subjects in applied probability and simulation. He is on the editorial board of Methodology and Computing in Applied Probability and is Guest Editor of the Annals of Operations Research. He has held research and teaching positions at Princeton University and The University of Melbourne, and is currently working at the Department of Mathematics of The University of Queensland. "Rarely have I seen such a dense and straight to the point pedagogical monograph on such a modern subject. This excellent book, on the simulated cross-entropy method (CEM) pioneered by one of the authors (Rubinstein), is very well written..." Computing Reviews, Stochastic Programming November, 2004 "It is a substantial contribution to stochastic optimization and more generally to the stochastic numerical methods theory." Short Book Reviews of the ISI, April 2005 "...I wholeheartedly recommend this book to anybody who is interested in stochastic optimization or simulation-based performance analysis of stochastic systems." Gazette of the Australian Mathematical Society, vol. 32 (3) 2005
Beschreibung:	1 Online-Ressource (XX, 301 p. 60 illus)
ISBN:	9781475743210
DOI:	10.1007/978-1-4757-4321-0