Understanding Computational Bayesian Statistics

There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods. In Bayesian statistics the rules of probability are used to make inferences about the parameter. Prior information about the parameter and sample information from the data are combined using Bayes theorem. Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used. This book uniquely covers the topics usually found in a typical introductory statistics book but from a Bayesian perspective.

WILLIAM M. BOLSTAD, PhD, is a Senior Lecturer in the Department of Statistics at the University of Waikato, New Zealand. He holds degrees from the University of Missouri, Stanford University, and the University of Waikato, New Zealand.