Simulation and the Monte Carlo method. (2016)
- Record Type:
- Book
- Title:
- Simulation and the Monte Carlo method. (2016)
- Main Title:
- Simulation and the Monte Carlo method.
- Authors:
- Rubinstein, Reuven Y
Kroese, Dirk P - Contents:
- Table of Contents Dedication Preface Acknowledgments Chapter 1: Preliminaries 1.1 Introduction 1.2 Random Experiments 1.3 Conditional Probability and Independence 1.4 Random Variables and Probability Distributions 1.5 Some Important Distributions 1.6 Expectation 1.7 Joint Distributions 1.8 Functions of Random Variables 1.9 Transforms 1.10 Jointly Normal Random Variables 1.11 Limit Theorems 1.12 Poisson Processes 1.13 Markov Processes 1.14 Gaussian Processes 1.15 Information 1.16 Convex Optimization and Duality Problems References Chapter 2: Random Number, Random Variable, and Stochastic Process Generation 2.1 Introduction 2.2 Random Number Generation 2.3 Random Variable Generation 2.4 Generating from Commonly Used Distributions 2.5 Random Vector Generation 2.6 Generating Poisson Processes 2.7 Generating Markov Chains and Markov Jump Processes 2.8 Generating Gaussian Processes 2.9 Generating Diffusion Processes 2.10 Generating Random Permutations Problems References Chapter 3: Simulation of Discrete-Event Systems 3.1 Introduction 3.2 Simulation Models 3.3 Simulation Clock and Event List for DEDS 3.4 Discrete-Event Simulation Problems References Chapter 4: Statistical Analysis of Discrete-Event Systems 4.1 Introduction 4.2 Estimators and Confidence Intervals 4.3 Static Simulation Models 4.4 Dynamic Simulation Models 4.5 The Bootstrap Method Problems References Chapter 5: Controlling the Variance 5.1 Introduction 5.2 Common and Antithetic Random Variables 5.3 Control VariablesTable of Contents Dedication Preface Acknowledgments Chapter 1: Preliminaries 1.1 Introduction 1.2 Random Experiments 1.3 Conditional Probability and Independence 1.4 Random Variables and Probability Distributions 1.5 Some Important Distributions 1.6 Expectation 1.7 Joint Distributions 1.8 Functions of Random Variables 1.9 Transforms 1.10 Jointly Normal Random Variables 1.11 Limit Theorems 1.12 Poisson Processes 1.13 Markov Processes 1.14 Gaussian Processes 1.15 Information 1.16 Convex Optimization and Duality Problems References Chapter 2: Random Number, Random Variable, and Stochastic Process Generation 2.1 Introduction 2.2 Random Number Generation 2.3 Random Variable Generation 2.4 Generating from Commonly Used Distributions 2.5 Random Vector Generation 2.6 Generating Poisson Processes 2.7 Generating Markov Chains and Markov Jump Processes 2.8 Generating Gaussian Processes 2.9 Generating Diffusion Processes 2.10 Generating Random Permutations Problems References Chapter 3: Simulation of Discrete-Event Systems 3.1 Introduction 3.2 Simulation Models 3.3 Simulation Clock and Event List for DEDS 3.4 Discrete-Event Simulation Problems References Chapter 4: Statistical Analysis of Discrete-Event Systems 4.1 Introduction 4.2 Estimators and Confidence Intervals 4.3 Static Simulation Models 4.4 Dynamic Simulation Models 4.5 The Bootstrap Method Problems References Chapter 5: Controlling the Variance 5.1 Introduction 5.2 Common and Antithetic Random Variables 5.3 Control Variables 5.4 Conditional Monte Carlo 5.5 Stratified Sampling 5.6 Multilevel Monte Carlo 5.7 Importance Sampling 5.8 Sequential Importance Sampling 5.9 Sequential Importance Resampling 5.10 Nonlinear Filtering for Hidden Markov Models 5.11 The Transform Likelihood Ratio Method 5.12 Preventing the Degeneracy of Importance Sampling Problems References Chapter 6: Markov Chain Monte Carlo 6.1 Introduction 6.2 The Metropolis{Hastings Algorithm 6.3 The Hit-and-Run Sampler 6.4 The Gibbs Sampler 6.5 Ising and Potts Models 6.6 Bayesian Statistics 6.7 Other Markov Samplers 6.8 Simulated Annealing 6.9 Perfect Sampling Problems References Chapter 7: Sensitivity Analysis and Monte Carlo Optimization 7.1 Introduction 7.2 The Score Function Method for Sensitivity Analysis of DESS 7.3 Simulation-Based Optimization of DESS 7.4 Sensitivity Analysis of DEDS Problems References Chapter 8: Cross-Entropy Method 8.1 Introduction 8.2 Estimation of Rare-Event Probabilities 8.3 The CE Method for Optimization 8.4 The Max-cut Problem 8.5 The Partition Problem 8.6 The Traveling Salesman Problem 8.7 Continuous Optimization 8.8 Noisy Optimization 8.9 The MinxEnt Method Problems References Chapter 9: Splitting Method 9.1 Introduction 9.2 Counting Self-Avoiding Walks via Splitting 9.3 Splitting with a Fixed Splitting Factor 9.4 Splitting with a Fixed Effort 9.5 Generalized Splitting 9.6 Adaptive Splitting 9.7 Application of Splitting to Network Reliability 9.8 Applications to Counting 9.9 Case Studies for Counting with Splitting 9.10 Splitting as a Sampling Method 9.11 Splitting for Optimization Problems References Chapter 10: Stochastic Enumeration Method 10.1 Introduction 10.2 Tree Search and Tree Counting 10.3 Knuth's Algorithm for Estimating the Cost of a Tree 10.4 Stochastic Enumeration 10.5 Application of SE to Counting 10.6 Application of SE to Network Reliability Problems References Appendix A.1 Cholesky Square Root Method A.2 Exact Sampling from a Conditional Bernoulli Distribution A.3 Exponential Families A.4 Sensitivity Analysis A.5 A Simple CE Algorithm for Optimizing the Peaks Function A.6 Discrete-time Kalman Filter A.7 Bernoulli Disruption Problem A.8 Complexity Problems References Abbreviations and Acronyms List of Symbols Index … (more)
- Edition:
- Third edition
- Publisher Details:
- Hoboken, New Jersey : John Wiley & Sons, Inc
- Publication Date:
- 2016
- Extent:
- 1 online resource
- Subjects:
- 518.282
Monte Carlo method
Digital computer simulation - Languages:
- English
- ISBNs:
- 9781118632383
9781118632208
9781118632284 - Related ISBNs:
- 9781118632161
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- Note: Description based on CIP data; item not viewed.
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- British Library HMNTS - ELD.DS.101812
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