Bayesian Essentials with R by Jean-Michel Marin

Bayesian Essentials with R by Jean-Michel Marin

“The material covered is perhaps quite ambitious and covers more than an introductory course in Bayesian statisticsPhD 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)

Sylvia Fr�hwirth-Schnatter is Professor of Applied Statistics and Econometrics at the Department of Finance, Accounting, and Statistics, Vienna University of Economics and Business, Austria. She has contributed to research in Bayesian modelling and MCMC inference for a broad range of models, including finite mixture and Markov switching models as well as state space models. She is particularly interested in applications of Bayesian inference in economics, finance, and business. She started to work on finite mixture and Markov switching models 20 years ago and has published more than 20 articles in this area in leading journals such as JASA, JCGS, and Journal of Applied Econometrics. Her monograph Finite Mixture and Markov Switching Models (2006) was awarded the Morris-DeGroot Price 2007 by ISBA. In 2014, she was elected Member of the Austrian Academy of Sciences.

Gilles Celeux is Director of research emeritus with INRIA Saclay-�le-de-France, France. He has conducted research in statistical learning, model-based clustering and model selection for more than 35 years and he leaded to Inria teams. His first paper on mixture modelling was written in 1981 and he is one of the co-organisators of the summer working group on model-based clustering since 1994. He has published more than 40 papers in international Journals of Statistics and wrote two textbooks in French on Classification. He was Editor-in-Chief of Statistics and Computing between 2006 and 2012 and he is the present Editor-in-Chief of the Journal of the French Statistical Society since 2012.

Christian P. Robert is Professor of Mathematics at CEREMADE, Universit� Paris-Dauphine, PSL Research University, France, and Professor of Statistics at the Department of Statistics, University of Warwick, UK. He has conducted research in Bayesian inference and computational methods covering Monte Carlo, MCMC, and ABC techniques, for more than 30 years, writing The Bayesian Choice (2001) and Monte Carlo Statistical Methods (2004) with George Casella. His first paper on mixture modelling was written in 1989 on radiograph image modelling. His fruitful collaboration with Mike Titterington on this topic spans two enjoyable decades of visits to Glasgow, Scotland. He has organised three conferences on the subject of mixture inference, with the last one at ICMS leading to the edited book Mixtures: Estimation and Applications (2011), co-authored with K. L. Mengersen and D. M. Titterington.