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- 2001053551 contributor B8959474.
- 2001053551 created "c2002.".
- 2001053551 date "2002".
- 2001053551 date "c2002.".
- 2001053551 dateCopyrighted "c2002.".
- 2001053551 description "Includes bibliographical references (p. 101-103).".
- 2001053551 description "Machine generated contents note: Chapter 1. Introduction 1 -- 1.1. What are Monte Carlo Methods? 1 -- 1.2. Review of Basic Probability and Statistics Terminology 5 --Chapter 2. Generating Random Numbers 11 -- 2.1. The General Problem 11 -- 2.2. Pseudo-Random Number Generators 13 -- 2.3. Generating Random Variates with Non-Uniform Distribution 17 --Chapter 3. Variance Reduction Techniques 29 -- 3.1. The Basic Problem 29 -- 3.2. Stratified Sampling 30 -- 3.3. Importance Sampling 36 -- 3.4. Common Random Numbers 42 -- 3.5. Control Variates 47 -- 3.6. Antithetic Variates 49 --Chapter 4. Markov Chain Monte Carlo 53 -- 4.1. Discrete Markov Chains 53 -- 4.2. Uniform Generation by Markov Chain Monte Carlo 58 -- 4.3. The Metropolis Algorithm 63 -- 4.4. Simulated Annealing 66 -- 4.5. The Gibbs Sampler 69 -- 4.6. The Gibbs Sampler in Bayesian Statistics 73 --Chapter 5. Statistical Analysis of Simulation Output 83 -- 5.1. The Case of I.I.D. Output 84 -- 5.2. The Case of Stationary Output 84 -- 5.3. The Case of Asymptotically Stationary Output 90 --Chapter 6. The Ising Model and Related Examples 93 -- 6.1. Definitions and Background 93 -- 6.2. Metropolis Algorithm and Gibbs Sampler 95 -- 6.3. Variants of the Ising Model 96 -- 6.4. Image Analysis - A Brief Example 97 -- 6.5. The Swendsen-Wang Algorithm 98.".
- 2001053551 extent "viii, 103 p. :".
- 2001053551 identifier "0821829785 (alk. paper)".
- 2001053551 identifier 2001053551.html.
- 2001053551 isPartOf "Fields Institute monographs".
- 2001053551 issued "2002".
- 2001053551 issued "c2002.".
- 2001053551 language "eng".
- 2001053551 publisher "Providence, R.I. : American Mathematical Society,".
- 2001053551 subject "519.2/82 21".
- 2001053551 subject "Monte Carlo method.".
- 2001053551 subject "QA298 .M33 2002".
- 2001053551 tableOfContents "Machine generated contents note: Chapter 1. Introduction 1 -- 1.1. What are Monte Carlo Methods? 1 -- 1.2. Review of Basic Probability and Statistics Terminology 5 --Chapter 2. Generating Random Numbers 11 -- 2.1. The General Problem 11 -- 2.2. Pseudo-Random Number Generators 13 -- 2.3. Generating Random Variates with Non-Uniform Distribution 17 --Chapter 3. Variance Reduction Techniques 29 -- 3.1. The Basic Problem 29 -- 3.2. Stratified Sampling 30 -- 3.3. Importance Sampling 36 -- 3.4. Common Random Numbers 42 -- 3.5. Control Variates 47 -- 3.6. Antithetic Variates 49 --Chapter 4. Markov Chain Monte Carlo 53 -- 4.1. Discrete Markov Chains 53 -- 4.2. Uniform Generation by Markov Chain Monte Carlo 58 -- 4.3. The Metropolis Algorithm 63 -- 4.4. Simulated Annealing 66 -- 4.5. The Gibbs Sampler 69 -- 4.6. The Gibbs Sampler in Bayesian Statistics 73 --Chapter 5. Statistical Analysis of Simulation Output 83 -- 5.1. The Case of I.I.D. Output 84 -- 5.2. The Case of Stationary Output 84 -- 5.3. The Case of Asymptotically Stationary Output 90 --Chapter 6. The Ising Model and Related Examples 93 -- 6.1. Definitions and Background 93 -- 6.2. Metropolis Algorithm and Gibbs Sampler 95 -- 6.3. Variants of the Ising Model 96 -- 6.4. Image Analysis - A Brief Example 97 -- 6.5. The Swendsen-Wang Algorithm 98.".
- 2001053551 title "Lectures on Monte Carlo methods / Neal Madras.".
- 2001053551 type "text".