Bayesian Essentials with R

This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications. 
Readers are empowered to participate in the real-life data analysis situations depicted here from the beginning. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book. In particular, all R codes are discussed with enough detail to make them readily understandable and expandable. 

Bayesian Essentials with R can be used as a textbook at both undergraduate and graduate levels. It is particularly useful with students in professional degree programs and scientists to analyze data the Bayesian way. The text will also enhance introductory courses on Bayesian statistics. Prerequisites for the book are an undergraduate background in probability and statistics, if not in Bayesian statistics. 

Sommario

User's Manual.- Normal Models.- Regression and Variable Selection.- Generalized Linear Models.- Capture-Recapture Experiments.- Mixture Models.- Time Series.- Image Analysis.- References.- Index.

Info autore

Christian P. Robert is Professor of Statistics in the Applied Mathematics Department at the Université Paris Dauphine, and external lecturer at Ecole Polytechnique, Palaiseau, France. He was previously Head of the Statistics Laboratory at the Center for Research in Economics and Statistics (CREST) of the National Institute for Statistics and Economic Studies (INSEE) in Paris. In addition to many papers on Bayesian statistics, simulation methods, and decision theory, he has written three other books.

Riassunto

This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications. 
Readers are empowered to participate in the real-life data analysis situations depicted here from the beginning. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book. In particular, all R codes are discussed with enough detail to make them readily understandable and expandable. 

Bayesian Essentials with R can be used as a textbook at both undergraduate and graduate levels. It is particularly useful with students in professional degree programs and scientists to analyze data the Bayesian way. The text will also enhance introductory courses on Bayesian statistics. Prerequisites for the book are an undergraduate background in probability and statistics, if not in Bayesian statistics. 

Testo aggiuntivo

“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)

Relazione

"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)

Dettagli sul prodotto

• Autori: Jean-Miche Marin, Jean-Michel Marin, Christian Robert, Christian P Robert, Christian P. Robert
• Editore: Springer, Berlin
• Lingue: Inglese
• Formato: Tascabile
• Pubblicazione: 01.01.2016
• EAN: 9781493950492
• ISBN: 978-1-4939-5049-2
• Pagine: 296
• Dimensioni: 157 mm x 234 mm x 22 mm
• Peso: 477 g
• Illustrazioni: XIV, 296 p. 75 illus., 38 illus. in color.
• Serie: Springer Texts in Statistics; Springer Texts in Statistics
• Categoria: Scienze naturali, medicina, informatica, tecnica > Matematica > Teoria delle probabilità, stocastica, statistica matematica