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Bayesian model selection and statist...

Ando, Tomohiro.

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Bayesian model selection and statistical modeling

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Bayesian model selection and statistical modeling/ Tomohiro Ando.
作者:	Ando, Tomohiro.
出版者:	Boca Raton, FL :Chapman & Hall/CRC, : c2010.,
面頁冊數:	1 online resource (xiv, 284 p.) :ill.
附註:	"A Chapman & Hall Book."
內容註:	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.
標題:	Bayes Theorem. -
電子資源:	http://www.crcnetbase.com/isbn/9781439836149
ISBN:	9781439836156 (electronic bk.)

Bayesian model selection and statistical modeling
Ando, Tomohiro.

Bayesian model selection and statistical modeling[electronic resource] /Tomohiro Ando. - Boca Raton, FL :Chapman & Hall/CRC,c2010. - 1 online resource (xiv, 284 p.) :ill. - Statistics : textbooks and monographs. - Statistics, textbooks and monographs..

"A Chapman & Hall Book."

Includes bibliographical references (p. 265-284).

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.

"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.

ISBN: 9781439836156 (electronic bk.)Subjects--Topical Terms:

709996
Bayes Theorem.
Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: QA279.5 / .A55 2010eb

Dewey Class. No.: 519.5/42

National Library of Medicine Call No.: QA 279.5

Bayesian model selection and statistical modeling
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300 $a 1 online resource (xiv, 284 p.) : $b ill.
490 1 $a Statistics : textbooks and monographs
500 $a "A Chapman & Hall Book."
504 $a Includes bibliographical references (p. 265-284).
505 0 $a 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.
520 $a "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.
588 $a Description based on print version record.
650 2 $a Bayes Theorem. $3 709996
650 2 $a Statistics as Topic. $3 2017472
650 2 $a Models, Theoretical. $3 594123
650 0 $a Bayesian statistical decision theory. $3 551404
650 0 $a Mathematical statistics. $3 516858
650 0 $a Mathematical models. $3 522882
655 0 $a Electronic books. $2 lcsh $3 542853
830 0 $a Statistics, textbooks and monographs. $3 1602183
856 4 0 $u http://www.crcnetbase.com/isbn/9781439836149