Along with many practical applications, Bayesian Model Selection
and Statistical Modeling presents an array of Bayesian inference
and model selection procedures. It thoroughly explains the
concepts, illustrates the derivations of various Bayesian model
selection criteria through examples, and provides R code for
implementation.
The author shows how to implement a variety of Bayesian
inference using R and sampling methods, such as Markov chain Monte
Carlo. He covers the different types of simulation-based Bayesian
model selection criteria, including the numerical calculation of
Bayes factors, the Bayesian predictive information criterion, and
the deviance information criterion. He also provides a theoretical
basis for the analysis of these criteria. In addition, the author
discusses how Bayesian model averaging can simultaneously treat
both model and parameter uncertainties.
Selecting and constructing the appropriate statistical model
significantly affect the quality of results in decision making,
forecasting, stochastic structure explorations, and other problems.
Helping you choose the right Bayesian model, this book focuses on
the framework for Bayesian model selection and includes practical
examples of model selection criteria.
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