The use of Bayesian methods in applied statistical analysis has becomeincreasingly popular, yet most introductory statistics texts continueto only present the subject using frequentist methods. Introduction to Bayesian Statistics, Second Editionfocuses on Bayesian methods that can be used for inference, and it alsoaddresses how these methods compare favorably with frequentistalternatives. Teaching statistics from the Bayesian perspective allowsfor direct probability statements about parameters, and this approachis now more relevant than ever due to computer programs that allowpractitioners to work on problems that contain many parameters.
This book uniquely covers the topics typically found in anintroductory statistics book—but from a Bayesian perspective—givingreaders an advantage as they enter fields where statistics is used.This Second Edition provides:

• Extended coverage of Poisson and Gamma distributions
• Two new chapters on Bayesian inference for Poisson observations andBayesian inference for the standard deviation for normal observations
• A twenty-five percent increase in exercises with selected answers at the end of the book
• A calculus refresher appendix and a summary on the use of statistical tables
• New computer exercises that use R functions and Minitab® macros for Bayesian analysis and Monte Carlo simulations