| Monte Carlo Methods in Bayesian Computation 2000. Corr. 2nd Edition Contributor(s): Chen, Ming-Hui (Author), Shao, Qi-Man (Author), Ibrahim, Joseph G. (Author) |
|
 |
ISBN: 0387989358 ISBN-13: 9780387989358 Publisher: Springer OUR PRICE: $104.49 Product Type: Hardcover - Other Formats Published: January 2000 Annotation: With an equal mix of theory and applications involving real data, this book presents the theoretical background of the Markov chain Monte Carlo (MCMC) methods and examines advanced Bayesian computational methods. 20 illus. |
| Additional Information |
| BISAC Categories: - Mathematics | Probability & Statistics - General |
| Dewey: 519.542 |
| LCCN: 99046366 |
| Series: Springer Series in Statistics |
| Physical Information: 0.97" H x 6.42" W x 9.54" (1.58 lbs) 387 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: Sampling from the posterior distribution and computing posterior quanti- ties of interest using Markov chain Monte Carlo (MCMC) samples are two major challenges involved in advanced Bayesian computation. This book examines each of these issues in detail and focuses heavily on comput- ing various posterior quantities of interest from a given MCMC sample. Several topics are addressed, including techniques for MCMC sampling, Monte Carlo (MC) methods for estimation of posterior summaries, improv- ing simulation accuracy, marginal posterior density estimation, estimation of normalizing constants, constrained parameter problems, Highest Poste- rior Density (HPD) interval calculations, computation of posterior modes, and posterior computations for proportional hazards models and Dirichlet process models. Also extensive discussion is given for computations in- volving model comparisons, including both nested and nonnested models. Marginal likelihood methods, ratios of normalizing constants, Bayes fac- tors, the Savage-Dickey density ratio, Stochastic Search Variable Selection (SSVS), Bayesian Model Averaging (BMA), the reverse jump algorithm, and model adequacy using predictive and latent residual approaches are also discussed. The book presents an equal mixture of theory and real applications. |