Applied Bayesian Hierarchical Methods by Peter D.,Congdon

By Peter D.,Congdon

The use of Markov chain Monte Carlo (MCMC) equipment for estimating hierarchical versions includes advanced information constructions and is usually defined as a innovative improvement. An intermediate-level therapy of Bayesian hierarchical types and their functions, Applied Bayesian Hierarchical Methods demonstrates some great benefits of a Bayesian method of info units concerning inferences for collections of comparable devices or variables and in tools the place parameters will be taken care of as random collections.

Emphasizing computational concerns, the ebook offers examples of the next program settings: meta-analysis, facts based in area or time, multilevel and longitudinal info, multivariate information, nonlinear regression, and survival time info. For the labored examples, the textual content ordinarily employs the WinBUGS package deal, permitting readers to discover substitute probability assumptions, regression buildings, and assumptions on past densities. It additionally contains BayesX code, that's quite invaluable in nonlinear regression. to illustrate MCMC sampling from first rules, the writer comprises labored examples utilizing the R package.

Through illustrative information research and a focus to statistical computing, this publication specializes in the sensible implementation of Bayesian hierarchical tools. It additionally discusses a number of matters that come up while making use of Bayesian innovations in hierarchical and random results models.

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