Bayesian Model Selection And Statistical Modeling - Info and Reading Options
By Tomohiro Ando
"Bayesian Model Selection And Statistical Modeling" was published by CRC Press in 2010 - Boca Raton and it has 300 pages.
“Bayesian Model Selection And Statistical Modeling” Metadata:
- Title: ➤ Bayesian Model Selection And Statistical Modeling
- Author: Tomohiro Ando
- Number of Pages: 300
- Publisher: CRC Press
- Publish Date: 2010
- Publish Location: Boca Raton
“Bayesian Model Selection And Statistical Modeling” Subjects and Themes:
- Subjects: ➤ Metody statystyczne - Statystyka Bayesa - Statystyka matematyczna - Modele matematyczne - Bayesian statistical decision theory - Mathematical statistics - Mathematical models - Statistics - Bayes Theorem - Statistics as Topic - Theoretical Models - Théorie de la décision bayésienne - Modèles mathématiques - Théorème de Bayes - Statistiques - MATHEMATICS - Probability & Statistics - Bayesian Analysis
Edition Identifiers:
- The Open Library ID: OL26130698M - OL17540839W
- Online Computer Library Center (OCLC) ID: 693771844
- Library of Congress Control Number (LCCN): 2010017141
- ISBN-13: 9781439836149
- All ISBNs: 9781439836149
AI-generated Review of “Bayesian Model Selection And Statistical Modeling”:
"Bayesian Model Selection And Statistical Modeling" Description:
Open Data:
"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."--Publisher's description
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