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. This book is intended to bring these techniques into the classยญ room, being (we hope) a self-contained logical development of the subject, with all concepts being explained in detail, and all theorems, etc. having detailed proofs. There is also an abundance of examples and problems, reยญ lating the concepts with statistical practice and enhancing primarily the application of simulation techniques to statistical problems of various difยญ ficulties. This is a textbook intended for a second-year graduate course. We do not assume that the reader has any familiarity with Monte Carlo techniques (such as random variable generation) or with any Markov chain theory. We do assume that the reader has had a first course in statistical theory at the level of Statistical Inference by Casella and Berger (1990). Unfortuยญ nately, a few times throughout the book a somewhat more advanced noยญ tion is needed. We have kept these incidents to a minimum and have posted warnings when they occur. While this is a book on simulation, whose actual implementation must be processed through a computer, no requirement is made on programming skills or computing abilities: algorithms are preยญ sented in a program-like format but in plain text rather than in a specific programming language. (Most of the examples in the book were actually implemented in C, with the S-Plus graphical interface
CONTENT
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 -- B.1 Mathematical -- B.2 Probability -- B.3 Distributions -- B.4 Markov Chains -- B.5 Statistics -- B.6 Algorithms -- C References -- Author Index
