Bayesian Statistics (Paperback)

by Peter M. Lee (Author)

Editorial Reviews

Review
Lee's book provides a reasonable introduction to Bayesian statistics - December 2004 -- Significance - the Royal Statistical Society Maga --This text refers to the Paperback edition.

Product Description
This volume provides full coverage of Bayesian statistics--perhaps the only fully self-consistent approach in statistics. The book furnishes an understandable treatment of the basic concepts and gives the reader useful information on where and why this somewhat controversial approach differs from "classical" statistics. The appendices include useful tables that are not readily available in other references. The book is based on a a highly successful lecture series for advanced undergraduates and fills a need for a text that is never too elemental nor too technical. --This text refers to an out of print or unavailable edition of this title.

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Product Details

• Paperback: 352 pages
• Publisher: John Wiley & Sons; 2 edition (September 1997)
• Language: English
• ISBN-10: 0471194816
• ISBN-13: 978-0471194811
• Product Dimensions: 9.2 x 6.2 x 0.8 inches
• Shipping Weight: 1.1 pounds
• Average Customer Review: 4.0 out of 5 stars See all reviews (5 customer reviews)
• Amazon.com Sales Rank: #7,130,490 in Books (See Bestsellers in Books)

Customer Reviews

Most Helpful Customer Reviews

56 of 56 people found the following review helpful:

4.0 out of 5 stars Good introduction to basic theory of Bayesian statistics, December 4, 2001

By	R. Daniel Meyer "rdme" (New Haven, CT United States) - See all my reviews (REAL NAME)

This review is from: Bayesian Statistics: An Introduction (Paperback)

This is a simple and easy-to-read introduction to the basics of Bayesian statistics, for someone with some previous exposure to statistical methods and theory. Lee does not try to do too much with this book. It's not too taxing on the brain, uses simple and easy-to-follow notation, and has a helpful appendix of common statistical distributions. I like the emphasis on conjugate priors, which are the mathematically most tractable Bayesian models that are often not treated fully in other texts. (Someone still needs to write the definitive text on conjugate Bayesian models.)

The book is limited in scope, a strength if you're just getting started on this topic, but will frustrate once you get into this stuff. There are plenty of other good books that go beyond the basics once you're ready.

26 of 26 people found the following review helpful:

5.0 out of 5 stars good intermediate text, January 22, 2008

By	Michael R. Chernick "statman31147" (Holland PA) - See all my reviews (REAL NAME)

This review is from: Bayesian Statistics: An Introduction (Paperback)

Although only the second edition is listed, I have read only the first 1989 edition and my review is for that edition. Lee wrote this book with the goal of teaching an introductory course in Bayesian statistics to his students at York University. He wanted a text that was more mathematical and deatiled than Lindley (1965) but not quite at the level of Box and Tiao.
This text achieves that goal. It was published at the time when MCMC methods were only starting to be appreciated. So the wider use of general prior distributions and hierarchical models does not yet enter into this book. I would assume that the second edition published in 1997 was written to remedy this shortcoming but I have not seen if it does.

But for the time it was a good intermediate text.

13 of 13 people found the following review helpful:

4.0 out of 5 stars a review, September 17, 2006

By	X. Zhang - See all my reviews (REAL NAME)

This review is from: Bayesian Statistics: An Introduction (Arnold Publication) (Paperback)

This book has a clean selection of materials as an introduction to bayesian statistics. It is quite readable. Two problems however: 1) the formula derivation and reasoning often have intermediate steps skipped. You need to think for a while for derivations and his texts. In particular, you need to figure out by yourself which theorem or previous results that the derivation is based on. 2) typos. the 3rd printing still has typos not listed in the author's page, not too many but not trivial either.

Anyway, I still recommand this book because no better introductory bayesian book found yet.