By Peter D.,Congdon
The use of Markov chain Monte Carlo (MCMC) tools for estimating hierarchical versions consists of advanced facts buildings and is frequently defined as a progressive improvement. An intermediate-level therapy of Bayesian hierarchical types and their purposes, Applied Bayesian Hierarchical Methods demonstrates some great benefits of a Bayesian method of information units concerning inferences for collections of similar devices or variables and in tools the place parameters may be taken care of as random collections.
Emphasizing computational matters, the booklet presents examples of the next software settings: meta-analysis, information dependent in area or time, multilevel and longitudinal information, multivariate info, nonlinear regression, and survival time info. For the labored examples, the textual content quite often employs the WinBUGS package deal, permitting readers to discover replacement probability assumptions, regression buildings, and assumptions on previous densities. It additionally comprises BayesX code, that's fairly valuable in nonlinear regression. to illustrate MCMC sampling from first rules, the writer contains labored examples utilizing the R package.
Through illustrative information research and a focus to statistical computing, this booklet makes a speciality of the sensible implementation of Bayesian hierarchical equipment. It additionally discusses a number of concerns that come up while using Bayesian thoughts in hierarchical and random results models.
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