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Introduction to Bayesian Statistics, 2nd Edition
John Wiley and Sons Ltd, Aug 2007, Pages: 437
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, now with increased exercises and coverage of a variety of Poisson techniques.
The use of Bayesian methods in applied statistical analysis has become increasingly popular, yet most introductory statistics texts continue to only present the subject using frequentist methods. Introduction to Bayesian Statistics, Second Edition focuses on Bayesian methods that can be used for inference, and it also addresses how these methods compare favorably with frequentist alternatives. Teaching statistics from the Bayesian perspective allows for direct probability statements about parameters, and this approach is now more relevant than ever due to computer programs that allow practitioners to work on problems that contain many parameters.
This book uniquely covers the topics typically found in an introductory statistics book-but from a Bayesian perspective-giving readers 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 and Bayesian 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(r) macros for Bayesian analysis and Monte Carlo simulations
- Introduction to Bayesian Statistics, Second Edition is an invaluable textbook for advanced undergraduate and graduate-level statistics courses as well as a practical reference for statisticians who require a working knowledge of Bayesian statistics.
About the author:
William M. Bolstad, PhD, is 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.?Dr. Bolstad's research interests include Bayesian statistics, MCMC methods, recursive estimation techniques, multiprocess dynamic time series models, and forecasting.
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