Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition (Chapman & Hall/CRC Texts in Statistical Science)
Major changes from the previous edition:
· More examples with discussion of computational details in chapters on Gibbs sampling and Metropolis-Hastings algorithms
· Recent developments in MCMC, including reversible jump, slice sampling, bridge sampling, path sampling, multiple-try, and delayed rejection
· Discussion of computation using both R and WinBUGS
· Additional exercises and selected solutions within the text, with all data sets and software available for download from the Web
· Sections on spatial models and model adequacy
The self-contained text units make MCMC accessible to scientists in other disciplines as well as statisticians. The book will appeal to everyone working with MCMC techniques, especially research and graduate statisticians and biostatisticians, and scientists handling data and formulating models. The book has been substantially reinforced as a first reading of material on MCMC and, consequently, as a textbook for modern Bayesian computation and Bayesian inference courses.