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"…excellent … for learning or applying [the Bayesian approach]. … wonderful case studies. Researchers and graduate students should read and familiarize with the software to practice Bayesian concepts."
—Journal of Statistical Computation and Simulation, Vol. 84, 2014

"One of its strongest features is the case studies for the different models. … the book clearly and accurately describes how to use BugsXLA. Each model and feature is illustrated with at least one case study. … this book and the Excel add-in BugsXLA make a nice contribution to the Bayesian ecosystem. They service the niche of applied researchers who want to join the Bayesian parade but find the learning curve of WinBUGS too steep to march up"
—Peter Lenk, The American Statistician, November 2013

"I think that the major strength of this book is the case studies section. The author uses a problem-solving approach, illustrating the methodology by focusing on the aims of the analysis. … The author’s style is rich but clear and comprehensible even for non-technical readers. … I would recommend this book to statisticians who already know Bayesian theory and MCMC algorithms and understand the use of specific statistical software like WinBUGS or R. For these readers, the book is an excellent manual and a precious source of suggestions to quickly and efficiently implement complex Bayesian linear models in Excel."—Journal of Agricultural, Biological, and Environmental Statistics, Volume 19, Number 1, 2013

"The author in writing this text has succeeded in making Bayesian analysis relatively simple through a graphical user interface (GUI) for WinBUGS—BugsXLA, which resides within Excel. … I recommend the book to anyone contemplating the use of Bayesian methods for the first time and already familiar with Excel for storing, summarizing and plotting basic statistical data. The text provides an ideal introduction to Bayesian approaches using Excel and ultimately will encourage the reader to migrate to WinBUGS proper."
—International Statistical Review, 80, 2012

"this book will benefit … applied statisticians who are familiar with applying generalised linear models and want to consider the impact of bringing Bayesian analyses into their work. … book will help a competent statistician to run a Bayesian analysis of a generalized linear mixed model almost effortlessly."
—John Paul Gosling, Journal of Applied Statistics, 2012

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