By Peter D.,Congdon
The use of Markov chain Monte Carlo (MCMC) tools for estimating hierarchical versions includes complicated facts constructions and is usually defined as a progressive improvement. An intermediate-level therapy of Bayesian hierarchical versions and their functions, Applied Bayesian Hierarchical Methods demonstrates some great benefits of a Bayesian method of facts units regarding inferences for collections of similar devices or variables and in tools the place parameters could be taken care of as random collections.
Emphasizing computational concerns, the publication offers examples of the next software settings: meta-analysis, information based in house or time, multilevel and longitudinal facts, multivariate information, nonlinear regression, and survival time information. For the labored examples, the textual content almost always employs the WinBUGS package deal, permitting readers to discover substitute chance assumptions, regression buildings, and assumptions on previous densities. It additionally accommodates BayesX code, that is relatively invaluable in nonlinear regression. to illustrate MCMC sampling from first rules, the writer comprises labored examples utilizing the R package.
Through illustrative info research and a focus to statistical computing, this booklet makes a speciality of the sensible implementation of Bayesian hierarchical tools. It additionally discusses numerous concerns that come up while utilizing Bayesian thoughts in hierarchical and random results models.
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Applied Bayesian Hierarchical Methods by Peter D.,Congdon