Monte Carlo Methods in Bayesian Computation

Dealing with methods for sampling from posterior distributions and how to compute posterior quantities of interest using Markov chain Monte Carlo samples, this book addresses such topics as improving simulation accuracy, marginal posterior density estimation, estimation of normalizing constants, constrained parameter problems, highest posterior density interval calculations, computation of posterior modes, and posterior computations for proportional hazards and Dirichlet process...
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Dealing with methods for sampling from posterior distributions and how to compute posterior quantities of interest using Markov chain Monte Carlo samples, this book addresses such topics as improving simulation accuracy, marginal posterior density estimation, estimation of normalizing constants, constrained parameter problems, highest posterior density interval calculations, computation of posterior modes, and posterior computations for proportional hazards and Dirichlet process models. The authors present an equal mixture of theory and applications involving real data, making this an ideal graduate textbook or reference for a one-semester course at the advanced masters or Ph.D. level. It also serves as a useful reference for applied or theoretical researchers as well as practitioners.