This new fourth edition deals with recent techniques such as Variation Methods, Bayesian Samples, Approximate Bayesian Calculus and Reversible Jump Markov Chain Monte Carlo (RJMCMC), and gives a brief overview of how Bayes’s approach to statistics develops It is in contrast to the conventional approach. The theory is built up step by step, and important terms such as sufficiency are taken out of the discussion of the salient features of particular examples.
Includes extended coverage of Gibbs sampling, including more numerical examples and treatments of OpenBUGS, R2WinBUGS, and R2Openbugs.
Presenting significant new material on newer techniques such as Bayes’s Samples, varying Bayes, Approximate Bayesian Computation (ABC) and Reversible Jump Markov Chain Monte Carlo (RJMCMC).
Provides extensive examples in the book to supplement the presented theory.
Accompanied by a supporting site with new materials and solutions.
More and more students are realizing that they need to learn Bayesian statistics to achieve their academic and professional goals. This book is best suited as the main text for courses in Bayesian statistics for third and fourth-year students and postgraduate students.