The use of Markov chain Monte Carlo (MCMC) methods for
estimating hierarchical models involves complex data structures and
is often described as a revolutionary development. An
intermediate-level treatment of Bayesian hierarchical models and
their applications, Applied Bayesian Hierarchical Methods
demonstrates the advantages of a Bayesian approach to data sets
involving inferences for collections of related units or variables
and in methods where parameters can be treated as random
collections.
Emphasizing computational issues, the book provides examples of
the following application settings: meta-analysis, data structured
in space or time, multilevel and longitudinal data, multivariate
data, nonlinear regression, and survival time data. For the worked
examples, the text mainly employs the WinBUGS package, allowing
readers to explore alternative likelihood assumptions, regression
structures, and assumptions on prior densities. It also
incorporates BayesX code, which is particularly useful in nonlinear
regression. To demonstrate MCMC sampling from first principles, the
author includes worked examples using the R package.
Through illustrative data analysis and attention to statistical
computing, this book focuses on the practical implementation of
Bayesian hierarchical methods. It also discusses several issues
that arise when applying Bayesian techniques in hierarchical and
random effects models.
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