Overview

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. 

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9781461486862

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Reviews

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

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 Corberan 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

Author Information

Jean-Michel Marin is Professor of Statistics at Université Montpellier 2, France, and Head of the Mathematics and Modelling research unit. He has written over 40 papers on Bayesian methodology and computing, as well as worked closely with population geneticists over the past ten years. Christian Robert is Professor of Statistics at Université Paris-Dauphine, France. He has written over 150 papers on Bayesian Statistics and computational methods and is the author or co-author of seven books on those topics, including The Bayesian Choice (Springer, 2001), winner of the ISBA DeGroot Prize in 2004. He is a Fellow of the Institute of Mathematical Statistics, the Royal Statistical Society and the American Statistical Society. He has been co-editor of the Journal of the Royal Statistical Society, Series B, and in the editorial boards of the Journal of the American Statistical Society, the Annals of Statistics, Statistical Science, and Bayesian Analysis. He is also a recipient of an Erskine Fellowship from the University of Canterbury (NZ) in 2006 and a senior member of the Institut Universitaire de France (2010-2015).

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