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Bayesian model selection and statistical modeling. (English) Zbl 1303.62006
Statistics: Textbooks and Monographs. Boca Raton, FL: CRC Press (ISBN 978-1-4398-3614-9/hbk; 978-1-4398-3615-6/ebook). xiv, 286 p. (2010).
Publisher’s description: Along with many practical applications, the book 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.

62-02 Research exposition (monographs, survey articles) pertaining to statistics
62C10 Bayesian problems; characterization of Bayes procedures
62C12 Empirical decision procedures; empirical Bayes procedures
62F15 Bayesian inference
62-04 Software, source code, etc. for problems pertaining to statistics
65C60 Computational problems in statistics (MSC2010)
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