Markov Chain Monte Carlo: Stochastic Simulation For Bayesian Inference (2nd Ed.)

摘要

and Monte Carlo tools, the book presents theory, methods and applications of MCMC, and other approximation tools of Bayesian inference in a concise and immensely readable manner. In the second edition, the authors included many new materials on applications, including chapters on spatial statistics, model diagnostics, and modern MCMC tools such as reversible jump, slice sampling, bridge sampling, path sampling, and delayed rejection. The great repository of R and WinBUGS code, accessible via the internet site of the book, enables readers to implement presented methods for many examples and exercises. The code can be modified for exploring further data examples. The book shows the authors' huge and practical experience on the subject of MCMC tools and their applications (particularly in dynamic modeling and generalized linear models). The vast array of exercises and examples in the book are an invaluable asset for the reader/instructor.