Author Description

"This book examines advanced Bayesian computational methods, it presents methods for sampling from posterior distributions and discusses how to compute posterior quantities of interest using Markov Chain Monte Carlo (MCMC) samples. This book examines each of these issues in detail and heavily focuses on computing various posterior summaries from a given MCMC sample.". "The book presents and equal mixture of theory and applications involving real data. It is intended as a graduate textbook or a reference book for a one-semester course at the advanced master's or Ph.D. level. It would also serve as a useful reference book for applied or theoretical researchers as well as practitioners."--BOOK JACKET.