A comprehensive account of the theory and application of Monte Carlo methods
Based on years of research in efficient Monte Carlo methods for estimation of rare-event probabilities, counting problems, and combinatorial optimization, Fast Sequential Monte Carlo Methods for Counting and Optimization is a complete illustration of fast sequential Monte Carlo techniques. The book provides an accessible overview of current work in the field of Monte Carlo methods, specifically sequential Monte Carlo techniques, for solving abstract counting and optimization problems.
Written by authorities in the field, the book places emphasis on cross-entropy, minimum cross-entropy, splitting, and stochastic enumeration. Focusing on the concepts and application of Monte Carlo techniques, Fast Sequential Monte Carlo Methods for Counting and Optimization includes:
Detailed algorithms needed to practice solving real-world problems
Numerous examples with Monte Carlo method produced solutions within the 1-2% limit of relative error
A new generic sequential importance sampling algorithm alongside extensive numerical results
An appendix focused on review material to provide additional background information
Fast Sequential Monte Carlo Methods for Counting and Optimization is an excellent resource for engineers, computer scientists, mathematicians, statisticians, and readers interested in efficient simulation techniques. The book is also useful for upper-undergraduate and graduate-level courses on Monte Carlo methods.
Additional information
Weight
0.426 kg
Product Properties
Year of Publication
2014
Table of Contents
Preface xi 1. Introduction to Monte Carlo Methods 1 2. Cross-Entropy Method 6 2.1. Introduction 6 2.2. Estimation of Rare-Event Probabilities 7 2.3. Cross-Entrophy Method for Optimization 18 2.4. Continuous Optimization 31 2.5. Noisy Optimization 33 3. Minimum Cross-Entropy Method 37 3.1. Introduction 37 3.2. Classic MinxEnt Method 39 3.3. Rare Events and MinxEnt 43 3.4. Indicator MinxEnt Method 47 3.5. IME Method for Combinatorial Optimization 52 4. Splitting Method for Counting and Optimization 56 4.1. Background 56 4.2. Quick Glance at the Splitting Method 58 4.3. Splitting Algorithm with Fixed Levels 64 4.4. Adaptive Splitting Algorithm 68 4.5. Sampling Uniformly on Discrete Regions 74 4.6. Splitting Algorithm for Combinatorial Optimization 75 4.7. Enhanced Splitting Method for Counting 76 4.8. Application of Splitting to Reliability Models 79 4.9. Numerical Results with the Splitting Algorithms 86 4.10. Appendix: Gibbs Sampler 104 5. Stochastic Enumeration Method 106 5.1. Introduction 106 5.2. OSLA Method and Its Extensions 110 5.3. SE Method 120 5.4. Applications of SE 127 5.5. Numerical Results 136 A. Additional Topics 148 A.1. Combinatorial Problems 148 A.1.1. Counting 149 A.1.2. Combinatorial Optimization 154 A.2. Information 162 A.2.1. Shannon Entropy 162 A.2.2. Kullback Leibler Cross-Entropy 163 A.3. Efficiency of Estimators 164 A.3.1. Complexity 165 A.3.2. Complexity of Randomized Algorithms 166 Bibliography 169 Abbreviations and Acronyms 177 List of Symbols 178 Index 181
Author
Reuven Y. Rubinstein
ISBN/ISSN
9781118612262
Binding
Hardback
Edition
1
Publisher
John Wiley
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