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. 

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