Monte Carlo Methods in Bayesian Computation is popular PDF and ePub book, written by Ming-Hui Chen in 2012-12-06, it is a fantastic choice for those who relish reading online the Mathematics genre. Let's immerse ourselves in this engaging Mathematics book by exploring the summary and details provided below. Remember, Monte Carlo Methods in Bayesian Computation can be Read Online from any device for your convenience.
Monte Carlo Methods in Bayesian Computation Book PDF Summary
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. The book presents an equal mixture of theory and applications involving real data, and is intended as a graduate textbook or a reference book for a one-semester course at the advanced masters or Ph.D. level. It will also serve as a useful reference for applied or theoretical researchers as well as practitioners.
Detail Book of Monte Carlo Methods in Bayesian Computation PDF
- Author : Ming-Hui Chen
- Release : 06 December 2012
- Publisher : Springer Science & Business Media
- ISBN : 9781461212768
- Genre : Mathematics
- Total Page : 399 pages
- Language : English
- PDF File Size : 7,8 Mb
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