By Peter D.,Congdon
The use of Markov chain Monte Carlo (MCMC) tools for estimating hierarchical versions includes advanced information constructions and is frequently 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 related to inferences for collections of similar devices or variables and in tools the place parameters could be handled as random collections.
Emphasizing computational concerns, the e-book presents examples of the next software settings: meta-analysis, information dependent in house or time, multilevel and longitudinal facts, multivariate information, nonlinear regression, and survival time facts. For the labored examples, the textual content frequently employs the WinBUGS package deal, permitting readers to discover substitute chance assumptions, regression buildings, and assumptions on earlier densities. It additionally accommodates BayesX code, that's relatively worthwhile in nonlinear regression. to illustrate MCMC sampling from first rules, the writer contains labored examples utilizing the R package.
Through illustrative info research and a focus to statistical computing, this publication specializes in the sensible implementation of Bayesian hierarchical equipment. It additionally discusses numerous concerns that come up whilst utilizing Bayesian innovations in hierarchical and random results models.
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Applied Bayesian Hierarchical Methods by Peter D.,Congdon