Description: Monte Carlo Methods in Bayesian Computation, Paperback by Chen, Ming-Hui; Shao, Qi-Man; Ibrahim, Joseph G., ISBN 146127074X, ISBN-13 9781461270744, Brand New, Free shipping in the US Dealing with methods for sampling from posterior distributions and how to compute posterior quantities of interest using Markov chain Monte Carlo (MCMC) samples, this book addresses such topics as 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 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. Th presents an equal mixture of theory and applications involving real data, and is intended as a graduate textbook or a referenc for a one-semester course at the advanced masters or . level. It will also serve as a useful reference for applied or theoretical researchers as well as practitioners.
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Book Title: Monte Carlo Methods in Bayesian Computation
Number of Pages: Xiii, 387 Pages
Language: English
Publication Name: Monte Carlo Methods in Bayesian Computation
Publisher: Springer New York
Publication Year: 2012
Item Height: 0.3 in
Subject: Biostatistics, Mathematical & Statistical Software, Probability & Statistics / Stochastic Processes, Probability & Statistics / General, Probability & Statistics / Bayesian Analysis
Type: Textbook
Item Weight: 21.8 Oz
Item Length: 9.3 in
Subject Area: Mathematics, Computers, Medical
Author: Ming-Hui Chen, Qi-Man Shao, Joseph G. Ibrahim
Item Width: 6.1 in
Series: Springer Series in Statistics Ser.
Format: Trade Paperback
