Monte Carlo Methods in Bayesian Computation

Ming-Hui Sheng, Qi-Man Shao, Joseph G. Ibrahim - Collection Springer Series In Statistics

386 pages, parution le 01/01/2000

Ajouter à une liste

Indisponible

Résumé

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 quantities of interest from a given MCMC sample. Several topics are addressed, including techniques for MCMC sampling, Monte Carlo methods for estimation of posterior quantities, improving simulation accuracy, marginal posterior density estimation, estimation of normalizing constants, constrained parameter problems, highest posterior density interval calculations, computation of posterior modes, and posterior computations for proportional hazards models and Dirichlet process models. The authors also discuss computions involving model comparisons, including both nested and non-nested models, marginal likelihood methods, ratios of normalizing constants, Bayes factors, the Savage-Dickey density ratio, Stochastic Search Variable Selection, Bayesian Model Averaging, the reverse jump algorithm, and model adequacy using predictive and latent residual approaches. The book presents an equal mixture of theory and applications involving real data. The book is intended as a graduate textbook or a reference book for a one semester course at the advanced masters or Ph.D. level. It would also serve as a useful reference book for applied or theoretical researchers as well as practitioners. Ming-Hui Chen is Associate Professor of Mathematical Sciences at Worcester Polytechnic Institute, Qu-Man Shao is Assistant Professor of Mathematics at the University of Oregon. Joseph G. Ibrahim is Associate Professor of Biostatistics at the Harvard School of Public Health and Dana-Farber Cancer Institute.

Sommaire

Markov Chain Monte Carlo Sampling
Basic Monte Carlo Methods for Estimating Posterior Quantities
Estimating Marginal Posterior Densities
Estimating Ratios of Normalizing Constants
Monte Carlo Methods for Constrained Parameter Problems
Computing Bayesian Credible and HPD Intervals
Bayesian Approaches for Comparing Non-Nested Models
Bayesian Variable Section
Other Topics

Voir tout

Replier

Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	Ming-Hui Sheng, Qi-Man Shao, Joseph G. Ibrahim
Collection	Springer Series In Statistics
Parution	01/01/2000
Nb. de pages	386
Format	16,3 x 24,2
Couverture	Relié
Poids	721g
Intérieur	Noir et Blanc
EAN13	9780387989358
ISBN13	978-0-387-98935-8