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Use R! Ser.: Bayesian Computation with R by Jim Albert (2009, Trade Paperback)

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About this product

Product Identifiers

PublisherSpringer New York

ISBN-100387922970

ISBN-139780387922973

eBay Product ID (ePID)71768298

Product Key Features

Number of PagesXii, 300 Pages

Publication NameBayesian Computation with R

LanguageEnglish

SubjectMathematical & Statistical Software, Numerical Analysis, Probability & Statistics / General, Probability & Statistics / Bayesian Analysis

Publication Year2009

TypeTextbook

Subject AreaMathematics, Computers

AuthorJim Albert

SeriesUse R! Ser.

FormatTrade Paperback

Dimensions

Item Weight34.2 Oz

Item Length9.3 in

Item Width6.1 in

Additional Product Features

Edition Number2

Intended AudienceScholarly & Professional

Dewey Edition22

Number of Volumes1 vol.

IllustratedYes

Dewey Decimal519.542

Table Of ContentAn Introduction to R.- to Bayesian Thinking.- Single-Parameter Models.- Multiparameter Models.- to Bayesian Computation.- Markov Chain Monte Carlo Methods.- Hierarchical Modeling.- Model Comparison.- Regression Models.- Gibbs Sampling.- Using R to Interface with WinBUGS.

SynopsisThere has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase in Bayesian activity by the number of published research articles, the number of books, andtheextensivenumberofapplicationsofBayesianarticlesinapplied disciplines such as science and engineering. One reason for the dramatic growth in Bayesian modeling is the availab- ity of computational algorithms to compute the range of integrals that are necessary in a Bayesian posterior analysis. Due to the speed of modern c- puters, it is now possible to use the Bayesian paradigm to 't very complex models that cannot be 't by alternative frequentist methods. To 't Bayesian models, one needs a statistical computing environment. This environment should be such that one can: write short scripts to de'ne a Bayesian model use or write functions to summarize a posterior distribution use functions to simulate from the posterior distribution construct graphs to illustrate the posterior inference An environment that meets these requirements is the R system. R provides a wide range of functions for data manipulation, calculation, and graphical d- plays. Moreover, it includes a well-developed, simple programming language that users can extend by adding new functions. Many such extensions of the language in the form of packages are easily downloadable from the Comp- hensive R Archive Network (CRAN), There has been a dramatic growth in the development and application of Bayesian inferential methods. This book introduces Bayesian modeling by the use of computation using the R language. The new edition contains changes in the R code illustrations., There has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase in Bayesian activity by the number of published research articles, the number of books, andtheextensivenumberofapplicationsofBayesianarticlesinapplied disciplines such as science and engineering. One reason for the dramatic growth in Bayesian modeling is the availab- ity of computational algorithms to compute the range of integrals that are necessary in a Bayesian posterior analysis. Due to the speed of modern c- puters, it is now possible to use the Bayesian paradigm to 't very complex models that cannot be 't by alternative frequentist methods. To 't Bayesian models, one needs a statistical computing environment. This environment should be such that one can: write short scripts to de?ne a Bayesian model use or write functions to summarize a posterior distribution use functions to simulate from the posterior distribution construct graphs to illustrate the posterior inference An environment that meets these requirements is the R system. R provides a wide range of functions for data manipulation, calculation, and graphical d- plays. Moreover, it includes a well-developed, simple programming language that users can extend by adding new functions. Many such extensions of the language in the form of packages are easily downloadable from the Comp- hensive R Archive Network (CRAN), There has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase in Bayesian activity by the number of published research articles, the number of books,andtheextensivenumberofapplicationsofBayesianarticlesinapplied disciplines such as science and engineering. One reason for the dramatic growth in Bayesian modeling is the availab- ity of computational algorithms to compute the range of integrals that are necessary in a Bayesian posterior analysis. Due to the speed of modern c- puters, it is now possible to use the Bayesian paradigm to ?t very complex models that cannot be ?t by alternative frequentist methods. To ?t Bayesian models, one needs a statistical computing environment. This environment should be such that one can: write short scripts to de'ne a Bayesian model use or write functions to summarize a posterior distribution use functions to simulate from the posterior distribution construct graphs to illustrate the posterior inference An environment that meets these requirements is the R system. R provides a wide range of functions for data manipulation, calculation, and graphical d- plays. Moreover, it includes a well-developed, simple programming language that users can extend by adding new functions. Many such extensions of the language in the form of packages are easily downloadable from the Comp- hensive R Archive Network (CRAN).

LC Classification NumberQA273.A1-274.9