- Hardcover: 464 pages
- Publisher: Wiley-Interscience; 2nd edition (August 15, 2007)
- Language: English
- ISBN-10: 0470141158
- ISBN-13: 978-0470141151
- Product Dimensions: 6.4 x 1.1 x 9.6 inches
- Shipping Weight: 1.6 pounds
- Average Customer Review: 17 customer reviews
- Amazon Best Sellers Rank: #813,260 in Books (See Top 100 in Books)
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"The general tenor of this book is good and it should serve well as a text for an introductory statistics course taught from a Bayesian perspective." (Biometrics, September 2008)
"Like the first edition, this edition is useful and effective in teaching Bayesian inference at both elementary and intermediate levels. It is a well-written book on elementary Bayesian inference, and the material is easily accessible. It is both concise and timely, and provides a good collection of overviews and reviews of important tools used in Bayesian statistical methods. (Technometrics, November 2008)
"Like the first edition, this edition is useful and effective in teaching Bayesian inference at both elementary and intermediate levels. It is a well-written book on elementary Bayesian inference, and the material is easily accessible. It is both concise and timely, and provides a good collection of overviews and reviews of important tools used in Bayesian statistical methods." (Technometrics, November 2008)
"Highly recommended. Upper-division undergraduates; graduate students; professionals." (CHOICE, April 2008)
From the Back Cover
Praise for the First Edition
"I cannot think of a better book for teachers of introductory statistics who want a readable and pedagogically sound text to introduce Bayesian statistics."
—Statistics in Medical Research
"[This book] is written in a lucid conversational style, which is so rare in mathematical writings. It does an excellent job of presenting Bayesian statistics as a perfectly reasonable approach to elementary problems in statistics."
—STATS: The Magazine for Students of Statistics, American Statistical Association
"Bolstad offers clear explanations of every concept and method making the book accessible and valuable to undergraduate and graduate students alike."
—Journal of Applied Statistics
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® 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.
Top customer reviews
Setting aside that such a book necessarily involves mathematics, the text itself is written in plain English and there are worked exampled to assist the reader in their learning of the concepts presented in each chapter. In addition, there are exercises at the end of each chapter, with answers provided for the odd-numbered ones.
If you were like me, studied statistics at university and barely covered Bayesian statistics, this is a good book for you. In particular, I think it is helpful that there are separate chapters on discrete and continuous applications of Bayesian statistics. While I have purchased this text specifically for a course, I believe that the book is suitable for readers who are not formally studying Bayesian statistics.
This may make me seem to be picky, but these errors are an absolute plague on the text. I found myself having to constantly reread sentences to trudge through these bizarre mistakes. The editing here is pretty amateur for a $125 book....
A common complaint about other Bayesian statistics books is that they seem to be written for people who already know the material, i.e. there's not really an effort to start with the basics and work up to the more complex material in a clear and accessible way. In contrast, Bolstad really does make an effort to teach people Bayesian statistics from the ground up.
The downside of this book, as reflected in some other reviews here, is that the material covered is not very advanced. The book doesn't even touch on Markov Chain Monte Carlo (MCMC) methods, for example, which form the backbone of much work in the field. So if you already know the basics of Bayesian statistics and want to know about MCMC methods and how to implement them, you would be better off with some of the recent books by Jim Albert, Jeff Gill, or Scott Lynch. Bolstad also has a more recent book "Understanding Computational Bayesian Statistics," but I haven't yet read it.
This book is perfect for someone who is either new to statistics or has a background in frequentist statistics and wants to see how Bayesian methods compare. There are very good and detailed discussions on frequentist vs. Bayesian hypothesis testing, for example.