Fast sequential Monte Carlo methods for counting and optimization /
This book presents the first comprehensive account of fast sequential Monte Carlo (SMC) methods for counting and optimization at an exceptionally accessible level. Written by authorities in the field, it places great emphasis on cross-entropy, minimum cross-entropy, splitting, and stochastic enumera...
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| Format: | Electronic eBook |
| Language: | English |
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Hoboken, New Jersey :
John Wiley & Sons, Inc.,
[2013]
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| Online Access: |
Full text (Emmanuel users only) |
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| 100 | 1 | |a Rubinstein, Reuven Y. | |
| 245 | 1 | 0 | |a Fast sequential Monte Carlo methods for counting and optimization / |c Reuven Rubinstein, Faculty of Industrial Engineering and Management, Technion, Israel Institute of Technology, Haifa, Israel, Ad Ridder, Department of Econometrics and Operations Research, Vrije University, Amsterdam, Netherlands, Radislav Vaisman, Faculty of Industrial Engineering and Management, Technion, Israel Institute of Technology, Haifa, Israel. |
| 264 | 1 | |a Hoboken, New Jersey : |b John Wiley & Sons, Inc., |c [2013] | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Series; Copyright; Dedication; Chapter 1: Introduction to Monte Carlo Methods; Chapter 2: Cross-Entropy Method; 2.1 Introduction; 2.2 Estimation of Rare-Event Probabilities; 2.3 Cross-Entropy Method forOptimization; 2.4 Continuous Optimization; 2.5 Noisy Optimization; Chapter 3: Minimum Cross-Entropy Method; 3.1 Introduction; 3.2 Classic MinxEnt Method; 3.3 Rare Events and MinxEnt; 3.4 Indicator MinxEnt Method; 3.5 IME Method for Combinatorial Optimization; Chapter 4: Splitting Method for Counting and Optimization; 4.1 Background; 4.2 Quick Glance at the Splitting Method. | |
| 505 | 8 | |a 4.3 Splitting Algorithm with Fixed Levels4.4 Adaptive Splitting Algorithm; 4.5 Sampling Uniformly on Discrete Regions; 4.6 Splitting Algorithm for Combinatorial Optimization; 4.7 Enhanced Splitting Method for Counting; 4.8 Application of Splitting to Reliability Models; 4.9 Numerical Results with the Splitting Algorithms; 4.10 Appendix: Gibbs Sampler; Chapter 5: Stochastic Enumeration Method; 5.1 Introduction; 5.2 OSLA Method and Its Extensions; 5.3 SE Method; 5.4 Applications of SE; 5.5 Numerical Results; Appendix A: Additional Topics; A.1 Combinatorial Problems; A.2 Information. | |
| 505 | 8 | |a A.3 Efficiency of EstimatorsBibliography; Abbreviations and Acronyms; List of Symbols; Index; Series. | |
| 520 | |a This book presents the first comprehensive account of fast sequential Monte Carlo (SMC) methods for counting and optimization at an exceptionally accessible level. Written by authorities in the field, it places great emphasis on cross-entropy, minimum cross-entropy, splitting, and stochastic enumeration. The overall aim is to make SMC methods accessible to readers who want to apply and to accentuate the unifying and novel mathematical ideas behind SMC in their future studies or work. | ||
| 588 | 0 | |a Print version record and CIP data provided by publisher. | |
| 650 | 0 | |a Monte Carlo method. | |
| 650 | 0 | |a Mathematical optimization. | |
| 650 | 2 | |a Monte Carlo Method | |
| 700 | 1 | |a Ridder, Ad, |d 1955- | |
| 700 | 1 | |a Vaisman, Radislav. | |
| 758 | |i has work: |a Fast sequential Monte Carlo methods for counting and optimization (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFFcBxPxJWWvBgjPM6cgKd |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
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