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Bayesian Model Selection and Statistical Modeling

Bayesian Model Selection and Statistical Modeling

Bayesian Model Selection and Statistical Modeling Paperback / softback - 2019

by Tomohiro Ando

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Ando 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.

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Paperback / softback. New. Ando 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.
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Details

  • Title Bayesian Model Selection and Statistical Modeling
  • Author Tomohiro Ando
  • Binding Paperback
  • Condition New
  • Pages 300
  • Volumes 1
  • Language ENG
  • Publisher CRC Press
  • Publication date 2019-09-19
  • Features Bibliography
  • Bookseller's Inventory # B9780367383978
  • ISBN 9780367383978 / 0367383977
  • Weight 0.94 lbs (0.43 kg)
  • Dimensions 9.21 x 6.14 x 0.63 in (23.39 x 15.60 x 1.60 cm)
  • Themes
    • Aspects (Academic): Science/Technology Aspects
  • Category Mathematics
  • Dewey Decimal Code 519.542
  • Quantity available 1

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From the publisher

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.

About the author

Tomohiro Ando is an associate professor of management science in the Graduate School of Business Administration at Keio University in Japan.

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