There are two major challenges involved in advanced Bayesian computation. These are how to sample from posterior distributions and how to compute posterior quantities of interest using Markov chain Monte Carlo (MCMC) samples. Several books, including Tanner (1996), Gilks, Richardson, and Spiegclhaltcr (1996), Gamerman (1997), Robert and Casella (1999), and Gelfand and Smith (2000), cover the development of MCMC sampling. Therefore, this book will provide only a quick but sufficient introduction to recently developed MCMC sampling techniques. In particular, the book will discuss several recently developed and useful computational tools in MCMC sampling which may not be presented in other existing MCMC books including those mentioned above.
KeywordsMarkov Chain Monte Carlo Monte Carlo Markov Chain Monte Carlo Sampling Reversible Jump Markov Chain Monte Carlo Bayesian Variable Selection
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