Introduction to Bayesian Statistics Reviewed
This book contains the best exposition I’ve encountered (so far) on the justification for Bayesian inference. The author should be applauded for not falling into the all-too-common trap of engaging in a purely syntactical derivation of Bayes’ Theorem from the definitions of conditional and joint probability. Unfortunately, the book doesn’t go far enough down the semantic path to make me a Bayesian ‘believer’. In the preface the author states, “In my book I do not wish to dwell on the problems associated with trying to be objective in Bayesian statistics.” Well, this is exactly what I’m interested in, so I would be delighted with a second book that deals directly and exclusively with this particular issue.
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Introduction to Bayesian Statistics Overview
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:
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Extended coverage of Poisson and Gamma distributions
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Two new chapters on Bayesian inference for Poisson observations and Bayesian inference for the standard deviation for normal observations
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A twenty-five percent increase in exercises with selected answers at the end of the book
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A calculus refresher appendix and a summary on the use of statistical tables
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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.
Best Buy Introduction to Bayesian Statistics :Customer Reviews
Pretty good and short – John Salvatier – Seattle, WA
This provides a good introduction to the basics of Bayesian statistics, and it’s not very long. No previous experience with statistics is required for this, but there are a lot of sections that can be skipped if you are already familiar with probability and statistics. I also recommend skipping the sections that compare Bayesian and frequentist methods unless you have studied a lot of frequentist statistics. I also recommend playing around with the R package (R is a free statistical programming language) that Bolstad provides.
I read the 1st edition of this book.
To learn more Bayesian statistics after this, you should probably read Bayesian Data Analysis by Gelman et. al.
A pedagogy gem – L. Antoine – Paris, France
Though quite expensive, this book is really a must-have for people with remote mathematical background needing to discover the bayesian approach. It is writtent as a complete course of introduction to statistics, but from the bayesian perspective. Personnaly, I never met the concepts so precisely explained, and would strongly advise anyone that wishes to be introduced with bayesian statistics to start here.
A must for beginners – student –
This books is an excellent introduction to any person interested on bayesian statistics. It provides straightforward explanations about the philosophy that supports bayesian statistics and its applications to credibility intervals, hypotesis testing and regression. After a first reading this book I finally understood conditional probabilities too!
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This book has been very helpful for my statistics class this year. The problem work ups are incredibly clear and easy to understand. I would definitely recommend this book.
