LEADER 05342nam 22007331 450 001 9910807943703321 005 20200520144314.0 010 $a1-118-61235-3 010 $a1-118-61232-9 010 $a1-118-61231-0 035 $a(CKB)2550000001159888 035 $a(EBL)1550546 035 $a(SSID)ssj0001053089 035 $a(PQKBManifestationID)11606606 035 $a(PQKBTitleCode)TC0001053089 035 $a(PQKBWorkID)11102996 035 $a(PQKB)10841291 035 $a(DLC) 2013019187 035 $a(Au-PeEL)EBL1550546 035 $a(CaPaEBR)ebr10799798 035 $a(CaONFJC)MIL543093 035 $a(CaSebORM)9781118612354 035 $a(MiAaPQ)EBC1550546 035 $a(OCoLC)843010592 035 $a(PPN)191455679 035 $a(EXLCZ)992550000001159888 100 $a20130506h20142014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aFast sequential Monte Carlo methods for counting and optimization /$fReuven Rubinstein, Ad Ridder, Radislav Vaisman 205 $a1st edition 210 1$aHoboken, New Jersey :$cJohn Wiley & Sons, Inc.,$d[2014] 210 4$dİ2014 215 $a1 online resource (208 p.) 225 0 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 $a1-118-61226-4 311 $a1-306-11842-5 320 $aIncludes bibliographical references and index. 327 $aCover; Title Page; Contents; Preface; 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-Entrophy Method for Optimization; 2.3.1. The Multidimensional 0/1 Knapsack Problem; 2.3.2. Mastermind Game; 2.3.3. Markov Decision Process and Reinforcement Learning; 2.4. Continuous Optimization; 2.5. Noisy Optimization; 2.5.1. Stopping Criterion; 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 327 $a3.4.1. Connection between CE and IME3.5. IME Method for Combinatorial Optimization; 3.5.1. Unconstrained Combinatorial Optimization; 3.5.2. Constrained Combinatorial Optimization: The Penalty Function Approach; Chapter 4 Splitting Method for Counting and Optimization; 4.1. Background; 4.2. Quick Glance at the Splitting Method; 4.3. Splitting Algorithm with Fixed Levels; 4.4. Adaptive Splitting Algorithm; 4.5. Sampling Uniformly on Discrete Regions; 4.6. Splitting Algorithm for Combinatorial Optimization; 4.7. Enhanced Splitting Method for Counting; 4.7.1. Counting with the Direct Estimator 327 $a4.7.2. Counting with the Capture-Recapture Method4.8. Application of Splitting to Reliability Models; 4.8.1. Introduction; 4.8.2. Static Graph Reliability Problem; 4.8.3. BMC Algorithm for Computing S(Y); 4.8.4. Gibbs Sampler; 4.9. Numerical Results with the Splitting Algorithms; 4.9.1. Counting; 4.9.2. Combinatorial Optimization; 4.9.3. Reliability Models; 4.10. Appendix: Gibbs Sampler; Chapter 5 Stochastic Enumeration Method; 5.1. Introduction; 5.2. OSLA Method and Its Extensions; 5.2.1. Extension of OSLA: nSLA Method; 5.2.2. Extension of OSLA for SAW: Multiple Trajectories; 5.3. SE Method 327 $a5.3.1. SE Algorithm5.4. Applications of SE; 5.4.1. Counting the Number of Trajectories in a Network; 5.4.2. SE for Probabilities Estimation; 5.4.3. Counting the Number of Perfect Matchings in a Graph; 5.4.4. Counting SAT; 5.5. Numerical Results; 5.5.1. Counting SAW; 5.5.2. Counting the Number of Trajectories in a Network; 5.5.3. Counting the Number of Perfect Matchings in a Graph; 5.5.4. Counting SAT; 5.5.5. Comparison of SE with Splitting and SampleSearch; Appendix A Additional Topics; A.1. Combinatorial Problems; A.1.1. Counting; A.1.2. Combinatorial Optimization; A.2. Information 327 $aA.2.1. Shannon EntropyA.2.2. Kullback-Leibler Cross-Entropy; A.3. Efficiency of Estimators; A.3.1. Complexity; A.3.2. Complexity of Randomized Algorithms; Bibliography; Abbreviations and Acronyms; List of Symbols; Index; Series Page 330 $a A comprehensive account of the theory and application of Monte Carlo methods Based on years of research in efficient Monte Carlo methods for estimation of rare-event probabilities, counting problems, and combinatorial optimization, Fast Sequential Monte Carlo Methods for Counting and Optimization is a complete illustration of fast sequential Monte Carlo techniques. The book provides an accessible overview of current work in the field of Monte Carlo methods, specifically sequential Monte Carlo techniques, for solving abstract counting and optimization problems. 410 0$aWiley Series in Probability and Statistics 606 $aMathematical optimization 606 $aMonte Carlo method 615 0$aMathematical optimization. 615 0$aMonte Carlo method. 676 $a518/.282 700 $aRubinstein$b Reuven Y$043545 701 $aRidder$b Ad$f1955-$01675585 701 $aVaisman$b Radislav$01675586 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910807943703321 996 $aFast sequential Monte Carlo methods for counting and optimization$94041180 997 $aUNINA