About this product

Product Identifiers

PublisherSpringer

ISBN-100387713840

ISBN-139780387713847

eBay Product ID (ePID)59098548

Product Key Features

Number of Pages267 Pages

LanguageEnglish

Publication NameBayesian Computation with R

SubjectProgramming Languages / General, Number Systems, Probability & Statistics / General, Computer Simulation, Probability & Statistics / Bayesian Analysis

Publication Year2007

TypeTextbook

Subject AreaMathematics, Computers

AuthorJim Albert

SeriesUse R Ser.

FormatPerfect

Dimensions

Item Height0.6 in

Item Weight13.9 Oz

Item Length9.1 in

Item Width6.8 in

Additional Product Features

Intended AudienceTrade

LCCN2007-929182

Dewey Edition22

IllustratedYes

Dewey Decimal519.542

Table Of ContentAn introduction to R.- Introduction to Bayesian thinking.- Single parameter models.- Multiparameter models.- Introduction to Bayesian computation.- Markov chain Monte Carlo methods.- Hierarchical modeling.- Model comparision.- Regression models.- Gibbs sampling.- Using R to interface with WinBUGS.

Synopsis'Bayesian Cmputation with R' introduces Bayesian modelling by the use of computation using the R language. The use of R to interface with WinBUGS, a popular MCMC computing language, is described with several illustrative examples., There has been a dramatic growth in the development and application of Bayesian inferential methods. Some of this growth is due to the availability of powerful simulation-based algorithms to summarize posterior distributions. There has been also a growing interest in the use of the system R for statistical analyses. R's open source nature, free availability, and large number of contributor packages have made R the software of choice for many statisticians in education and industry. Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. The early chapters present the basic tenets of Bayesian thinking by use of familiar one and two-parameter inferential problems. Bayesian computational methods such as Laplace's method, rejection sampling, and the SIR algorithm are illustrated in the context of a random effects model. The construction and implementation of Markov Chain Monte Carlo (MCMC) methods is introduced. These simulation-based algorithms are implemented for a variety of Bayesian applications such as normal and binary response regression, hierarchical modeling, order-restricted inference, and robust modeling. Algorithms written in R are used to develop Bayesian tests and assess Bayesian models by use of the posterior predictive distribution. The use of R to interface with WinBUGS, a popular MCMC computing language, is described with several illustrative examples. This book is a suitable companion book for an introductory course on Bayesian methods. Also the book is valuable to the statistical practitioner who wishes to learn more about the R language and Bayesian methodology. The LearnBayes package, written by the author andavailable from the CRAN website, contains all of the R functions described in the book., Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. Early chapters present the basic tenets of Bayesian thinking by use of familiar one and two-parameter inferential problems. Bayesian computational methods such as Laplace's method, rejection sampling, and the SIR algorithm are illustrated in the context of a random effects model. The construction and implementation of Markov Chain Monte Carlo (MCMC) methods is introduced. These simulation-based algorithms are implemented for a variety of Bayesian applications such as normal and binary response regression, hierarchical modeling, order-restricted inference, and robust modeling. Algorithms written in R are used to develop Bayesian tests and assess Bayesian models by use of the posterior predictive distribution. The use of R to interface with WinBUGS, a popular MCMC computing language, is described with several illustrative examples.

LC Classification NumberQA279.5