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Abstract:
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1. Introduction -- 2. Random Variable Generation -- 3. Monte Carlo Integration -- 4. Markov Chains -- 5. Monte Carlo Optimization -- 6. The Metropolis-Hastings Algorithm -- 7. The Gibbs Sampler -- 8. Diagnosing Convergence -- 9. Implementation in Missing Data Models -- A. Probability Distributions -- B. Notation.
"Monte Carlo statistical methods, particularly those based on Markov chains, have now matured to be part of the standard set of techniques used by statisticians. Written as a self-contained logical development of the subject, this book will be suitable as an introduction to the field or as a textbook intended for a second-year graduate course. The reader is not assumed to have any familiarity with Monte Carlo techniques (such as random variable generation), or with any Markov chain theory."--BOOK JACKET.
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