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Introduction to Bayesian Statistics, by William M. Bolstad
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Traditionally, introductory statistics courses have been taught from a frequentist perspective. The recent upsurge in the use of Bayesian methods in applied statistical analysis highlights the need to expose students early on to the Bayes theorem, its advantages, and its applications. Based on the author’s successful courses, Introduction to Bayesian Statistics introduces statistics from a Bayesian perspective in a way that is understandable to readers with a reasonable mathematics background.
Covering most of the same ground found in a typical statistics book–but from a Bayesian perspective–Introduction to Bayesian Statistics offers thorough, clearly-explained discussions of:
- Scientific data gathering, including the use of random sampling methods and randomized experiments to make inferences on cause-effect relationships
- The rules of probability, including joint, marginal, and conditional probability
- Discrete and continuous random variables
- Bayesian inferences for means and proportions compared with the corresponding frequentist ones
- The simple linear regression model analyzed in a Bayesian manner
To assist in the understanding of Bayesian statistics, this introduction provides readers with exercises (with selected answers); summaries of main points from each chapter; a calculus refresher, and a summary on the use of statistical tables; and R functions and Minitab macros for Bayesian analysis and Monte Carlo simulations (downloadable from the associated Web site)
- Sales Rank: #1613232 in Books
- Published on: 2004-04-26
- Original language: English
- Number of items: 1
- Dimensions: 9.49" h x .96" w x 6.46" l,
- Binding: Hardcover
- 376 pages
Review
"I would recommend this book if you are interested in teaching an introductory in Bayesian statistics…" (The American Statistician, February 2006)
"…a very useful undergraduate text presenting a novel approach to an introductory statistics course." (Biometrics, September 2005)
"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, October 2005)
"…this book fills a gap for teaching elementary Bayesian statistics…it could easily serve as a self-learning text…" (Technometrics, May 2005)
[In a review comparing Bolstad with another book,] "I will keep both of these books on my shelf, but I expect that Bolstad will be the one most borrowed by my colleagues."(significance, December 2004)
"...does an excellent job of presenting Bayesian Statistics as a perfectly reasonable approach to elementary problems of statistics…I must heartily recommend this book…" (STATS: The Magazine for Students of Statistics, Fall 2004)
About the Author
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.
Most helpful customer reviews
49 of 50 people found the following review helpful.
A Great Foundation for Learning Bayesian Statistics
By David Ciemiewicz
For people who need a primer in Statistics, especially Bayesian Statistics, I'd recommend this book.
When you are finished with this book, you can apply basic Bayesian methods for common case scenarios involving Normal distributions and Binomial distributions. Pragmatic scenarios for understanding how to interpret the results and understand when your prior may be inappropriate for your data were quite welcome and missing or underrepresented in other books and seminars I've taken in Bayesian statistics.
I especially appreciated the discussions on how to perform hypothesis tests in a Bayesian framework that is rare to find.
Bolstad does an excellent job in showing the relationship between Bayesian and Frequentist methods but, in my opinion, doesn't do enough exclamation points in the cases where he shows that they mathematically converge.
What is especially GOOD about this book is the fact that it DOESN'T get into the heavy mathematical underpinnings, history, and rationale for Frequentist vs Bayesian approaches. I have taken several seminars and read several books which focus on this approach of introducing rigorous mathematical formalism and integration using Markov Chain Monte Carlo (MCMC) was left somewhat bewildered by it. The PhD's who worked with me and tried to explain why Bayesian methods were better utterly failed because they overemphasized the high-falutin' mathematical rigor In fact, I would go so far as to say that the university professors and statisticians who emphasize these techniques are actually holding back the advancement and use of Bayesian methods by the general practicioners because of this. The average person doing statistics is going to do it by rote (whether we like it or not) so providing accessible methods to people who can do this is the way to further the cause.
Bolstad's book is a great foundation because it doesn't try to be comprehensive and mathematically rigorous book. It focuses on providing just enough mathematical underpinnings to understand the basic concepts and make progress without dwelling on it.
It would be great if Bolstad's book were used in high schools or in freshman college and university courses to introduce statistics. We'd convert more people to Bayesian methods if we did.
27 of 28 people found the following review helpful.
Intro to Bayesian Bolstad
By George C. Newman
This is an outstanding introduction for anyone with a modest knowledge of algebra. It includes some of the clearest expositions of fundamental statistical concepts and then extends and re-interprets those concepts in a way that makes Bayesian logic natural and intuitive. Excellent problems to solve illustrating and solidifying the concepts of each chapter. First rate reading!
0 of 0 people found the following review helpful.
Decent intro book
By ram
decent intro book with a fair amount of theory for those that like that sort of thing. It would be nice to have some R code to go with the exercises.
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