By Peter D.,Congdon
The use of Markov chain Monte Carlo (MCMC) tools for estimating hierarchical types consists of complicated info buildings and is usually defined as a progressive improvement. An intermediate-level therapy of Bayesian hierarchical versions and their purposes, Applied Bayesian Hierarchical Methods demonstrates the benefits of a Bayesian method of info units regarding inferences for collections of similar devices or variables and in equipment the place parameters will be taken care of as random collections.
Emphasizing computational matters, the booklet offers examples of the subsequent program settings: meta-analysis, information established in house or time, multilevel and longitudinal info, multivariate facts, nonlinear regression, and survival time facts. For the labored examples, the textual content ordinarily employs the WinBUGS package deal, permitting readers to discover replacement chance assumptions, regression buildings, and assumptions on previous densities. It additionally comprises BayesX code, that's quite invaluable in nonlinear regression. to illustrate MCMC sampling from first ideas, the writer comprises labored examples utilizing the R package.
Through illustrative information research and a focus to statistical computing, this ebook specializes in the sensible implementation of Bayesian hierarchical tools. It additionally discusses numerous matters that come up whilst making use of Bayesian thoughts in hierarchical and random results models.
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Applied Bayesian Hierarchical Methods by Peter D.,Congdon