Description
Bayesian Model Selection and Statistical Modeling
Author: Ando Tomohiro
Language: EnglishSubjects for Bayesian Model Selection and Statistical Modeling:
Keywords
Posterior Distribution; Marginal Likelihood; posterior; Bayes Factor; distribution; Bayesian Model Selection Criteria; marginal; Intrinsic Bayes Factor; likelihood; BIC Score; probabilities; Posterior Model Probabilities; factor; Conditional Posterior; inference; Reversible Jump MCMC; predictive; Fractional Bayes Factor; expected; Expected Log Likelihood; log; Conditional Posterior Density; Gibb’s Sampling; Posterior Mode; SUR Model; Conditional Posterior Distribution; Savage Dickey Density Ratio; Posterior Samples; Predictive Distribution; Generalized Information Criterion; Posterior Density; Bayesian Model Averaging; Smoothing Parameter; Quantile Regression; BMA Approach
Publication date: 06-2010
Support: Print on demand
Publication date: 09-2019
· 15.6x23.4 cm · Paperback
Description
/li>Contents
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/li>Biography
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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.
Introduction. Introduction to Bayesian Analysis. Asymptotic Approach for Bayesian Inference. Computational Approach for Bayesian Inference. Bayesian Approach for Model Selection. Simulation Approach for Computing the Marginal Likelihood. Various Bayesian Model Selection Criteria. Theoretical Development and Comparisons. Bayesian Model Averaging. Bibliography. Index.
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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