A First Course in Monte Carlo Simulation
Published on 1. October 2005
Book
Paperback/Softback
350 pages
978-0-495-01878-0 (ISBN)
Description
A COURSE IN MONTE CARLO is a concise explanation of the Monte Carlo (MC) method. In addition to providing guidance for generating samples from diverse distributions, it describes how to design, perform, and analyze the results of MC experiments based on independent replications, Markov chain MC, and MC optimization. The text gives considerable emphasis to the variance-reducing techniques of importance sampling, stratified sampling, Rao-Blackwellization, control variates, antithetic variates, and quasi-random numbers. For solving optimization problems it describes several MC techniques, including simulated annealing, simulated tempering, swapping, stochastic tunneling, and genetic algorithms. Examples from many areas show how these techniques perform in practice. Hands-on exercises enable student to experience challenges encountered when solving real problems. An answer key to selected problems is included.
Reviews / Votes
1. INTRODUCTION. About this Book. Integration and Summation. Improving Efficiency. Minimizing a Function. Improving Efficiency. Reading Plans. Exercises. Software. Notations. References. 2. INDEPENDENT MONTE CARLO. Independent Monte Carlo (IMC). Why Monte Carlo?. Generating Samples. Choosing a Monte Carlo Sampling Plan. Importance Sampling. Estimating Volume. Interpreting Relative Error. Product and Non-Product Spaces. Bootstrap Method. Regression Analysis. Lessons Learned. Hands-On Exercises. References. 3. SAMPLING GENERATION. Selecting a Sampling Algorithm. Independent and Dependent Variates. Inverse-Transform Method. Restricted Sampling. Discrete Distributions. Sampling from a Table. Restricted Sampling. Composition Method. Acceptance-Rejection Method. Squeeze Method. Adaptive Method. Ratio-of-Uniforms Method. Lessons Learned. Exercises. References. 4. PSEUDORANDOM NUMBER GENERATION. Linear Congruential Generators. Prime Modulus. Evaluating PNG's. Theoretical Evaluation. Empirical Testing. Collision Test. Birthday Spacings Test. LCG's with Modulus 2(Beta).M = 2(superscript)32.M = 2(superscript)48. Mixed Linear Congruential Generators. Combined Generators. AWC and SWB Generators. Twisted GFSR Generators. Mersenne Twisted GFSRs. Lessons Learned. References. 5. VARIANCE REDUCTION. Stratified Sampling. Unequal Sample Sizes. Rao-Blackwellization. Exceedance Probabilities for Rare Events. Control Variates. Antithetic Variates. Quasirandom Numbers. Exercises. Lessons Learned. Hands-On Exercises. References. 6. MARKOV CHAIN MONTE CARLO. Hastings-Metropolis Method. Reversibility. Coordinate Updating. Single-Coordinate Updating. Bayesian MCMC. Joint and Full Conditional Distributions. Gibbs Sampling. Convergence for Xj. Non-connected State Spaces and Mixtures. Convergence for (Lambda)t. Local and Global Moves. Problem Size. Variance of Estimate. Choosing a Nominating Kernel. Independence Hastings-Metropolis Sampling. Random-Walk Nominating Kernels. Chains Favoring Smaller (Sigma)(infinity)squared. General-State Spaces. Polynomial Convergence. Discrete-Event Systems. First-Passage Times. Absorbing States. Lessons Learned. Appendix: Modified Acceptance-Rejection Sampling. Appendix: Modified Acceptance-Rejection Sampling. Exercises. Hands-On Exercises. References. 7. MCMC SAMPLE-PATH ANALYSIS. Multiple Independent Replications. Single Sample Path. Estimating the Warm-Up Interval. Batch-Means Method. FNB Rule. SQRT Rule. ABATCH Rule. Testing for Independence. Appendix: LABATCH.2. Lessons Learned. Hands-On Exercises. References. 8. OPTIMIZATION VIA MCMC. Searching for the Global Optimum. Nominating Kernels. Cooling. Initial Temperature. Temperature Gradient. Stage Length. Stopping Rule. Local Minima. Accelerating Convergence. Simulated Tempering. Swapping. Stochastic Tunneling. Genetic Algorithms. Searching for More Than the Minimum. Lessons Learned. Hands-On Exercises. References. 9. ADVANCED CONCEPTS IN MCMC. Exploiting Reversibility. Rapid Mixing. Markov Random Fields. Gibbs Distribution. Potts Model. Random Cluster Model. Problem Size. More General Models. Slice Sampling. Product Slice Sampling. Partial Decoupling. Coupling from the Past. Monotone Markov Chains. Reusing Randomness. Total Number of Steps. Saving Space. Independent Hastings-Metropolis Sampling. Lessons Learned. Exercises. References.More details
Language
English
Place of publication
CA
United States
Publishing group
Cengage Learning, Inc
Target group
College/higher education
Professional and scholarly
Dimensions
Height: 228 mm
Width: 185 mm
Thickness: 17 mm
Weight
636 gr
ISBN-13
978-0-495-01878-0 (9780495018780)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Person
George Fishman has regularly contributed to the literature on the Monte Carlo method and discrete-event simulation over the last 40 years. His earlier book, MONTE CARLO: CONCEPTS, ALGORITHMS, AND APPLICATIONS (Springer-Verlag, 1996) won the 1996 Lancaster award for best publication of the Institute for Operations Research and Management Sciences (INFORMS) and the 1997 outstanding publication award of the INFORMS College on Simulation.
Content
1. INTRODUCTION. About this Book. Integration and Summation. Improving Efficiency. Minimizing a Function. Improving Efficiency. Reading Plans. Exercises. Software. Notations. References. 2. INDEPENDENT MONTE CARLO. Independent Monte Carlo (IMC). Why Monte Carlo?. Generating Samples. Choosing a Monte Carlo Sampling Plan. Importance Sampling. Estimating Volume. Interpreting Relative Error. Product and Non-Product Spaces. Bootstrap Method. Regression Analysis. Lessons Learned. Hands-On Exercises. References. 3. SAMPLING GENERATION. Selecting a Sampling Algorithm. Independent and Dependent Variates. Inverse-Transform Method. Restricted Sampling. Discrete Distributions. Sampling from a Table. Restricted Sampling. Composition Method. Acceptance-Rejection Method. Squeeze Method. Adaptive Method. Ratio-of-Uniforms Method. Lessons Learned. Exercises. References. 4. PSEUDORANDOM NUMBER GENERATION. Linear Congruential Generators. Prime Modulus. Evaluating PNG"s. Theoretical Evaluation. Empirical Testing. Collision Test. Birthday Spacings Test. LCG"s with Modulus 2(Beta).M = 2(superscript)32.M = 2(superscript)48. Mixed Linear Congruential Generators. Combined Generators. AWC and SWB Generators. Twisted GFSR Generators. Mersenne Twisted GFSRs. Lessons Learned. References. 5. VARIANCE REDUCTION. Stratified Sampling. Unequal Sample Sizes. Rao-Blackwellization. Exceedance Probabilities for Rare Events. Control Variates. Antithetic Variates. Quasirandom Numbers. Exercises. Lessons Learned. Hands-On Exercises. References. 6. MARKOV CHAIN MONTE CARLO. Hastings-Metropolis Method. Reversibility. Coordinate Updating. Single-Coordinate Updating. Bayesian MCMC. Joint and Full Conditional Distributions. Gibbs Sampling. Convergence for Xj. Non-connected State Spaces and Mixtures. Convergence for (Lambda)t. Local and Global Moves. Problem Size. Variance of Estimate. Choosing a Nominating Kernel. Independence Hastings-Metropolis Sampling. Random-Walk Nominating Kernels. Chains Favoring Smaller (Sigma)(infinity)squared. General-State Spaces. Polynomial Convergence. Discrete-Event Systems. First-Passage Times. Absorbing States. Lessons Learned. Appendix: Modified Acceptance-Rejection Sampling. Appendix: Modified Acceptance-Rejection Sampling. Exercises. Hands-On Exercises. References. 7. MCMC SAMPLE-PATH ANALYSIS. Multiple Independent Replications. Single Sample Path. Estimating the Warm-Up Interval. Batch-Means Method. FNB Rule. SQRT Rule. ABATCH Rule. Testing for Independence. Appendix: LABATCH.2. Lessons Learned. Hands-On Exercises. References. 8. OPTIMIZATION VIA MCMC. Searching for the Global Optimum. Nominating Kernels. Cooling. Initial Temperature. Temperature Gradient. Stage Length. Stopping Rule. Local Minima. Accelerating Convergence. Simulated Tempering. Swapping. Stochastic Tunneling. Genetic Algorithms. Searching for More Than the Minimum. Lessons Learned. Hands-On Exercises. References. 9. ADVANCED CONCEPTS IN MCMC. Exploiting Reversibility. Rapid Mixing. Markov Random Fields. Gibbs Distribution. Potts Model. Random Cluster Model. Problem Size. More General Models. Slice Sampling. Product Slice Sampling. Partial Decoupling. Coupling from the Past. Monotone Markov Chains. Reusing Randomness. Total Number of Steps. Saving Space. Independent Hastings-Metropolis Sampling. Lessons Learned. Exercises. References.