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Introduction to Bayesian Statistics, 2nd Edition 2nd Edition

4.3 out of 5 stars 17 customer reviews
ISBN-13: 978-0470141151
ISBN-10: 0470141158
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Product Details

  • 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 (View shipping rates and policies)
  • Average Customer Review: 4.3 out of 5 stars  See all reviews (17 customer reviews)
  • Amazon Best Sellers Rank: #697,011 in Books (See Top 100 in Books)

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Customer Reviews

Top Customer Reviews

By Gabriel Murray on October 28, 2010
Format: Hardcover
Bolstad's Introduction to Bayesian Statistics is the gold standard for clear and accessible introductions to the topic. If you've read other Bayesian books and felt perplexed within the first few pages, this is the book for you. It's suitable for the novice with little or no statistics background, as the first few chapters cover the basics of the field. There is even an appendix introducing the essentials of calculus, in case you haven't studied calculus or need to brush up. The book features many examples worked through in detail to take the mystery out of the material.

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.
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Format: Hardcover Verified Purchase
The book's content presentation is fine, but it is absolutely riddled with errors. The errata [1] for this second edition is 9 pages long (!!!) and even at that absurd length it is incomplete. I am not joking when I say that there is typically an error on every other page of this book. I do not believe that this book was edited at all whatsoever before it was printed. Maybe this would be acceptable for a self-published work of fiction, but as a professional textbook for such a high price this is ridiculous. The author and publisher should be ashamed that they released a textbook with such low editorial quality.

[1] http://www.stats.waikato.ac.nz/publications/bolstad/IntroductionToBayesianStatistics/students/Second%20Edition/errata.pdf
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Format: Hardcover
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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Format: Hardcover Verified Purchase
A very good introduction to the field of Bayesian statistics for graduate students. The author expects the student to have basic knowledge of the usual frequentist statistics, in particular probability theory, and a firm knowledge of undergraduate calculus. This book by Bolstad introduces and thoroughly explains all the Bayesian posterior inference instrumentation for separate and conjugate distributions like the beta-binomial, the Poisson-gamma, the normal and their applications. In all chapters the differences between frequentist and Bayesian statistical inference is highlighted and discussed. In every chapter the student finds a number of more theoretical exercises, more or less the same as the examples, which can be solved without computer aid, and a number of exercises that needs the availability of Minitab and the R-language. I think the book could have done by the without-computer exercises alone, because the computer exercises don't seem to offer the reader much more understanding of the material. But this is a minor point. Of course, the student can skip those exercises if he wants to do so. In short: a very good book that I can recommend to professors for their graduate students.
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Format: Hardcover Verified Purchase
The book is just what it says, an introduction to Bayesian statistics. Some prior statistics knowledge would be helpful, for example in understanding what a cumulative density function is, and while some calculus knowledge would allow the reader to undertake some calculations manually, the book comes with Minitab macros and an R package to supplement the content of those programs for the calculus grunt work. There is one appendix each on working with the Minitab macros and R package. I'm using R and the package is working fine with the 64-bit version of R 2.12.1.

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.
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