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Bayesian Hierarchical Models - With Applications Using R, Second Edition (Paperback, 2nd edition)
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Bayesian Hierarchical Models - With Applications Using R, Second Edition (Paperback, 2nd edition)
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An intermediate-level treatment of Bayesian hierarchical models and
their applications, this book 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. Through illustrative data
analysis and attention to statistical computing, this book
facilitates practical implementation of Bayesian hierarchical
methods. The new edition is a revision of the book Applied Bayesian
Hierarchical Methods. It maintains a focus on applied modelling and
data analysis, but now using entirely R-based Bayesian computing
options. It has been updated with a new chapter on regression for
causal effects, and one on computing options and strategies. This
latter chapter is particularly important, due to recent advances in
Bayesian computing and estimation, including the development of
rjags and rstan. It also features updates throughout with new
examples. The examples exploit and illustrate the broader
advantages of the R computing environment, while allowing readers
to explore alternative likelihood assumptions, regression
structures, and assumptions on prior densities. Features: Provides
a comprehensive and accessible overview of applied Bayesian
hierarchical modelling Includes many real data examples to
illustrate different modelling topics R code (based on rjags,
jagsUI, R2OpenBUGS, and rstan) is integrated into the book,
emphasizing implementation Software options and coding principles
are introduced in new chapter on computing Programs and data sets
available on the book's website
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