This book presents a collaborative approach to data analysis involving both scientists and statisticians that can be used to model unknown parameters using scientific information (apriori). Keeping the mathematics to a minimum, the text focuses on applications, statistical ideas, models and interpretations. It features WinBUGS throughout the computational problems and uses Monte Carlo methods for all simulations.
This is a very sound introductory text, and is certainly one which teachers of any course on Bayesian statistics beyond the briefest and most elementary should consider adopting. --David J. Hand, International Statistical Review (2011), 79 Unlike many Bayesian books which did not cover this topic extensively, this new book teaches readers how to illicit informative priors from field experts in great detail. ! Straightforward R codes are also provided for pinpointing hyperparameter values ! this book is particularly valuable in emphasizing the right approach to elicit prior, an important component of deriving posterior or predictive distribution. Another important feature of this new Bayesian textbook is its rich details. !The proofs never skip steps, and are easy to follow for readers taking only one or two semester math stat classes. The well-written text along with more than 70 figures and 50 plus tables add tremendously to the elucidation of the problems discussed in the book. Directly following some examples or important discussion in the text, readers can self-check whether they understand the materials by playing with some exercise problems, most of which are pretty straightforward. Christensen et al. provide many WinBUGS codes in the book and a website for readers to download these codes. In addition, the authors introduce how to perform Bayesian inferences using SAS codes on two occasions ! The book also recommends some other programs or websites that will facilitate computation ! This book is also characterized by its humor, ! [making] reading this Bayesian book more delightful. --Dunlei Cheng, Statistics in Medicine, 2011
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