Introduction to Bayesian statistics /

Bolstad (Univ. of Waikato, New Zealand) offers an easy-to-read book on Bayesian statistics, written in a very pleasant style. This new edition differs from the first (CH, Dec'04, 42-2264) in that it includes two new chapters (10 and 15) besides renumbering other chapters, adding new exercises (including computer exercises using Minitab Macros and R-functions, downloadable from the Web site noted in the book). Of 16 chapters, the first three introduce topics and describe data gathering techniques and methods for displaying and summarizing data. Other chapters include basic concepts of probability, Bayes's theorem, discrete random variables, Bayesian inference and discrete random variables with discrete priors, continuous random variables, binomial proportions; Bayesian inference for proportion with corresponding frequentist via mean squared error, for Poisson distributions with continuous priors and normal mean with discrete priors as well as continuous priors; comparing Bayesian and frequentist inferences about the means of normal populations; Bayesian inference for difference between means of two normal populations, difference between proportions, and paired comparisons; Bayesian techniques for simple linear regression; Bayesian inference for standard deviation of normal populations, and on making Bayesian inferences more robust. Numerous worked-out examples; chapter exercises; appendixes with calculus; statistical tables; usage of Minitab Macros and R-functions. Summing Up: Highly recommended. Upper-division undergraduates; graduate students; professionals. D. V. Chopra Wichita State University