Overview
- Includes supplementary material: sn.pub/extras
Part of the book series: Springer Series in Statistics (SSS)
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About this book
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.
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Table of contents (10 chapters)
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Front Matter
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Back Matter
Reviews
"This book combines the theory topics with good computer and application examples from the field of food science, agriculture, cancer and others. The volume will provide an excellent research resource for statisticians with an interest in computer intensive methods for modelling with different sorts of prior information."
A.V. Tsukanov in "Short Book Reviews", Vol. 20/3, December 2000
A.V. Tsukanov in "Short Book Reviews", Vol. 20/3, December 2000
Authors and Affiliations
Bibliographic Information
Book Title: Monte Carlo Methods in Bayesian Computation
Authors: Ming-Hui Chen, Qi-Man Shao, Joseph G. Ibrahim
Series Title: Springer Series in Statistics
DOI: https://doi.org/10.1007/978-1-4612-1276-8
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media New York 2000
Hardcover ISBN: 978-0-387-98935-8Published: 21 January 2000
Softcover ISBN: 978-1-4612-7074-4Published: 04 October 2012
eBook ISBN: 978-1-4612-1276-8Published: 06 December 2012
Series ISSN: 0172-7397
Series E-ISSN: 2197-568X
Edition Number: 1
Number of Pages: XIII, 387
Topics: Probability Theory and Stochastic Processes, Statistical Theory and Methods, Statistics for Life Sciences, Medicine, Health Sciences, Statistics and Computing/Statistics Programs