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Bayesian Essentials with R by Christian P. Robert (English) Paperback Book
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eBay item number:395144481320
Item specifics
- Condition
- ISBN-13
- 9781493950492
- Book Title
- Bayesian Essentials with R
- ISBN
- 9781493950492
- Subject Area
- Computers, Mathematics
- Publication Name
- Bayesian Essentials with R
- Item Length
- 9.2 in
- Publisher
- Springer New York
- Subject
- Mathematical & Statistical Software, Probability & Statistics / General, Probability & Statistics / Bayesian Analysis
- Series
- Springer Texts in Statistics Ser.
- Publication Year
- 2016
- Type
- Textbook
- Format
- Trade Paperback
- Language
- English
- Item Height
- 0.7 in
- Item Width
- 6.1 in
- Item Weight
- 167.4 Oz
- Number of Pages
- Xiv, 296 Pages
About this product
Product Information
An ideal text for applied statisticians needing a standalone introduction to computational Bayesian statistics, this work by a renowned authority on the subject focuses on standard models backed up by real datasets. It includes an inclusive R (CRAN) package.
Product Identifiers
Publisher
Springer New York
ISBN-10
1493950495
ISBN-13
9781493950492
eBay Product ID (ePID)
235056863
Product Key Features
Publication Name
Bayesian Essentials with R
Format
Trade Paperback
Language
English
Subject
Mathematical & Statistical Software, Probability & Statistics / General, Probability & Statistics / Bayesian Analysis
Series
Springer Texts in Statistics Ser.
Publication Year
2016
Type
Textbook
Subject Area
Computers, Mathematics
Number of Pages
Xiv, 296 Pages
Dimensions
Item Length
9.2 in
Item Height
0.7 in
Item Width
6.1 in
Item Weight
167.4 Oz
Additional Product Features
Edition Number
2
Intended Audience
Trade
Number of Volumes
1 Vol.
Lc Classification Number
Qa276.4-.45
Reviews
"This book is a very helpful and useful introduction to Bayesian methods of data analysis. I found the use of R, the code in the book, and the companion R package, bayess, to be helpful to those who want to begin using Bayesian methods in data analysis. ... Overall this is a solid book and well worth considering by its intended audience." (David E. Booth, Technometrics, Vol. 58 (3), August, 2016) "Jean-Michel Marin's and Christian P. Robert's book Bayesian Essentials with R provides a wonderful entry to statistical modeling and Bayesian analysis. ... Overall, this is a well-written and concise book that combines theoretical ideas with a wide range of practical applications in an excellent way. Consequently, it can be highly useful to researchers who need to use Bayesian tools to analyze their datasets and professors who have to teach or students enrolled in an introductory course on Bayesian statistics." (Ana Corberán Vallet, Biometrical Journal, Vol. 58 (2), 2016), This text focuses on the process of Bayesian analysis by integrating Bayesian theory, methods and computing to solve real data applications. Remarkably it accomplishes this in a straightforward, easy-to-understand manner. It starts with an introduction to Bayesian methods in simple normal models and ends with sophisticated applications in image analysis. Each chapter includes real data applications and extensive R code implementing the methods, all of which is included in the associated R package bayess. The text is ideally suited for use as an introduction to Bayesian methods and computing in undergraduate classes. - Galin Jones, School of Statistics, University of Minnesota Bayesian Essentials can be split in two parts: i) basic linear and generalized linear models, after a concise and useful introduction to the related R package, and ii) more advanced modeling structures, such as mixtures, time series and image analysis. For graduate students this book will be useful when reading chapters or sections and then running the accompanying R package bayess. -Hedibert Freitas Lopes, Professor of Statistics and Econometrics, INSPER Institute of Education and Research", "The material covered is perhaps quite ambitious and covers more than an introductory course in Bayesian statistics. PhD students and all those who want to check the computational details of the Bayesian approach will find the book very useful and interesting. A lot of researchers using Bayesian approaches only through Winbugs will perhaps find this book as an excellent companion of how the methods work really and gain insight from this." (Dimitris Karlis, zbMATH 1380.62005, 2018) "This book is a very helpful and useful introduction to Bayesian methods of data analysis. I found the use of R, the code in the book, and the companion R package, bayess, to be helpful to those who want to begin using Bayesian methods in dataanalysis. ... Overall this is a solid book and well worth considering by its intended audience." (David E. Booth, Technometrics, Vol. 58 (3), August, 2016) "Jean-Michel Marin's and Christian P. Robert's book Bayesian Essentials with R provides a wonderful entry to statistical modeling and Bayesian analysis. ... Overall, this is a well-written and concise book that combines theoretical ideas with a wide range of practical applications in an excellent way. Consequently, it can be highly useful to researchers who need to use Bayesian tools to analyze their datasets and professors who have to teach or students enrolled in an introductory course on Bayesian statistics." (Ana Corberán Vallet, Biometrical Journal, Vol. 58 (2), 2016), This book is a very helpful and useful introduction to Bayesian methods of data analysis. I found the use of R, the code in the book, and the companion R package, bayess, to be helpful to those who want to begin using Bayesian methods in data analysis. Overall this is a solid book and well worth considering by its intended audience. (David E. Booth, Technometrics, Vol. 58 (3), August, 2016)", "This book is a very helpful and useful introduction to Bayesian methods of data analysis. I found the use of R, the code in the book, and the companion R package, bayess, to be helpful to those who want to begin using Bayesian methods in data analysis. ... Overall this is a solid book and well worth considering by its intended audience." (David E. Booth, Technometrics, Vol. 58 (3), August, 2016) "Jean-Michel Marin's and Christian P. Robert's book Bayesian Essentials with R provides a wonderful entry to statistical modeling and Bayesian analysis. ... Overall, this is a well-written and concise book that combines theoretical ideas with a wide range of practical applications in an excellent way. Consequently, it can be highly useful to researchers who need to use Bayesian tools to analyze their datasets and professors who have to teach or students enrolled in an introductory course on Bayesian statistics." (Ana Corbern Vallet, Biometrical Journal, Vol. 58 (2), 2016), "The material covered is perhaps quite ambitious and covers more than an introductory course in Bayesian statistics. PhD students and all those who want to check the computational details of the Bayesian approach will find the book very useful and interesting. A lot of researchers using Bayesian approaches only through Winbugs will perhaps find this book as an excellent companion of how the methods work really and gain insight from this." (Dimitris Karlis, zbMATH 1380.62005, 2018) "This book is a very helpful and useful introduction to Bayesian methods of data analysis. I found the use of R, the code in the book, and the companion R package, bayess, to be helpful to those who want to begin using Bayesian methods in data analysis. ... Overall this is a solid book and well worth considering by its intended audience." (David E. Booth, Technometrics, Vol. 58 (3), August, 2016) "Jean-Michel Marin's and Christian P. Robert's book Bayesian Essentials with R provides a wonderful entry to statistical modeling and Bayesian analysis. ... Overall, this is a well-written and concise book that combines theoretical ideas with a wide range of practical applications in an excellent way. Consequently, it can be highly useful to researchers who need to use Bayesian tools to analyze their datasets and professors who have to teach or students enrolled in an introductory course on Bayesian statistics." (Ana Corberán Vallet, Biometrical Journal, Vol. 58 (2), 2016)
Table of Content
User's Manual.- Normal Models.- Regression and Variable Selection.- Generalized Linear Models.- Capture-Recapture Experiments.- Mixture Models.- Time Series.- Image Analysis.- References.- Index.
Copyright Date
2014
Illustrated
Yes
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