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The Resource Bayesian Computation with R, by Jim Albert, (electronic resource)
Bayesian Computation with R, by Jim Albert, (electronic resource)
Resource Information
The item Bayesian Computation with R, by Jim Albert, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Oklahoma Libraries.This item is available to borrow from all library branches.
Resource Information
The item Bayesian Computation with R, by Jim Albert, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Oklahoma Libraries.
This item is available to borrow from all library branches.
 Summary
 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 simulationbased 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 twoparameter 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 simulationbased algorithms are implemented for a variety of Bayesian applications such as normal and binary response regression, hierarchical modeling, orderrestricted 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 and 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 and available from the CRAN website, contains all of the R functions described in the book. The second edition contains several new topics such as the use of mixtures of conjugate priors and the use of Zellner’s g priors to choose between models in linear regression. There are more illustrations of the construction of informative prior distributions, such as the use of conditional means priors and multivariate normal priors in binary regressions. The new edition contains changes in the R code illustrations according to the latest edition of the LearnBayes package. Jim Albert is Professor of Statistics at Bowling Green State University. He is Fellow of the American Statistical Association and is past editor of The American Statistician. His books include Ordinal Data Modeling (with Val Johnson), Workshop Statistics: Discovery with Data, A Bayesian Approach (with Allan Rossman), and Bayesian Computation using Minitab
 Language

 eng
 eng
 Edition
 Second edition
 Extent
 1 online resource (299 pages)
 Note
 Description based upon print version of record
 Contents

 An Introduction to R
 to Bayesian Thinking
 SingleParameter 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
 Isbn
 9780387922980
 Label
 Bayesian Computation with R
 Title
 Bayesian Computation with R
 Statement of responsibility
 by Jim Albert
 Subject

 Mathematical Software
 Computer software
 Visualization
 Mathematical statistics
 Distribution (Probability theory
 Computational Mathematics and Numerical Analysis
 Visualization
 Probability Theory and Stochastic Processes
 Statistical Theory and Methods
 Computer simulation
 Simulation and Modeling
 Computer science  Mathematics
 Language

 eng
 eng
 Summary
 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 simulationbased 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 twoparameter 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 simulationbased algorithms are implemented for a variety of Bayesian applications such as normal and binary response regression, hierarchical modeling, orderrestricted 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 and 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 and available from the CRAN website, contains all of the R functions described in the book. The second edition contains several new topics such as the use of mixtures of conjugate priors and the use of Zellner’s g priors to choose between models in linear regression. There are more illustrations of the construction of informative prior distributions, such as the use of conditional means priors and multivariate normal priors in binary regressions. The new edition contains changes in the R code illustrations according to the latest edition of the LearnBayes package. Jim Albert is Professor of Statistics at Bowling Green State University. He is Fellow of the American Statistical Association and is past editor of The American Statistician. His books include Ordinal Data Modeling (with Val Johnson), Workshop Statistics: Discovery with Data, A Bayesian Approach (with Allan Rossman), and Bayesian Computation using Minitab
 http://library.link/vocab/creatorName
 Albert, Jim
 Dewey number
 519.542
 http://bibfra.me/vocab/relation/httpidlocgovvocabularyrelatorsaut
 KLm5tzdAGg
 Language note
 English
 LC call number

 QA273.A1274.9
 QA274274.9
 Literary form
 non fiction
 Nature of contents
 dictionaries
 Series statement
 Use R!,
 http://library.link/vocab/subjectName

 Distribution (Probability theory
 Computer science
 Computer software
 Mathematical statistics
 Computer simulation
 Visualization
 Probability Theory and Stochastic Processes
 Computational Mathematics and Numerical Analysis
 Mathematical Software
 Statistical Theory and Methods
 Simulation and Modeling
 Visualization
 Label
 Bayesian Computation with R, by Jim Albert, (electronic resource)
 Note
 Description based upon print version of record
 Bibliography note
 Includes bibliographical references (p. [259]262) and index
 Carrier category
 online resource
 Carrier category code
 cr
 Carrier MARC source
 rdacarrier
 Content category

 text
 still image
 Content type code

 txt
 sti
 Content type MARC source

 rdacontent
 rdacontent
 Contents
 An Introduction to R  to Bayesian Thinking  SingleParameter 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
 Dimensions
 unknown
 Edition
 Second edition
 Extent
 1 online resource (299 pages)
 Form of item
 online
 Isbn
 9780387922980
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code
 c
 Other control number
 10.1007/9780387922980
 Other physical details
 illustrations
 Specific material designation
 remote
 System control number

 (CKB)1000000000746275
 (EBL)437823
 (OCoLC)405547793
 (SSID)ssj0000289705
 (PQKBManifestationID)11205564
 (PQKBTitleCode)TC0000289705
 (PQKBWorkID)10401892
 (PQKB)10919438
 (DEHe213)9780387922980
 (MiAaPQ)EBC437823
 (EXLCZ)991000000000746275
 Label
 Bayesian Computation with R, by Jim Albert, (electronic resource)
 Note
 Description based upon print version of record
 Bibliography note
 Includes bibliographical references (p. [259]262) and index
 Carrier category
 online resource
 Carrier category code
 cr
 Carrier MARC source
 rdacarrier
 Content category

 text
 still image
 Content type code

 txt
 sti
 Content type MARC source

 rdacontent
 rdacontent
 Contents
 An Introduction to R  to Bayesian Thinking  SingleParameter 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
 Dimensions
 unknown
 Edition
 Second edition
 Extent
 1 online resource (299 pages)
 Form of item
 online
 Isbn
 9780387922980
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code
 c
 Other control number
 10.1007/9780387922980
 Other physical details
 illustrations
 Specific material designation
 remote
 System control number

 (CKB)1000000000746275
 (EBL)437823
 (OCoLC)405547793
 (SSID)ssj0000289705
 (PQKBManifestationID)11205564
 (PQKBTitleCode)TC0000289705
 (PQKBWorkID)10401892
 (PQKB)10919438
 (DEHe213)9780387922980
 (MiAaPQ)EBC437823
 (EXLCZ)991000000000746275
Subject
 Computational Mathematics and Numerical Analysis
 Computer science  Mathematics
 Computer simulation
 Computer software
 Distribution (Probability theory
 Mathematical Software
 Mathematical statistics
 Probability Theory and Stochastic Processes
 Simulation and Modeling
 Statistical Theory and Methods
 Visualization
 Visualization
Member of
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