Overview

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.
ISBN-13

9781461270744

ISBN-10

146127074X

Weight

1.25 Pounds

Dimensions

6.10 x 0.91 x 9.25 In

List Price

$109.99

Edition

1st Edition

Format

Paperback

Language

English

Pages

xiii, 387 pages

Publisher

Springer

Published On

2012-10-04


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Alibris
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Sparks, NV, USA

Print on demand Trade paperback (US). Glued binding. 387 p. Springer Statistics.
$112.98

 Free delivery by: 31 Mar 2026


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