Good introduction to basic theory of Bayesian statistics | Bayesian Statistics: An Introduction (Arnold...

a review | Bayesian Statistics: An Introduction (Arnold Publication) | Peter M. Lee

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	• Bayesian Statistics: An Introduction (Arnold Publication) Peter M. Lee A Hodder Arnold Publication, 2004 - 368 pages average customer review:based on 5 reviews view larger image
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highly recommended

Very Dense book with nonstandard notation

This book appears to have all the basics for Bayesian Statistics, but the notation is very different from most statistics texts. For example, theta is usually the mean, as opposed to the standard mu, and phi is the variance, as opposed to sigma^2. It's also not a light read, although most graduate level mathematics/engineering texts arent as well.

Good introduction to basic theory of Bayesian statistics

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.

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a review

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.

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good intermediate text

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.

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good intermediate text

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.

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Among statisticians the Bayesian approach continues to gain adherents and this new edition of Peter Lee's classic introduction maintains the clarity of exposition and use of examples for which the text is known and praised. In addition, there is extended coverage of the Metropolis-Hastings algorithm as well as an introduction to the use of BUGS, as this is now the standard computational tool for such numerical work. Other alterations include new material on generalized linear modeling and Bernardo's theory of reference points.

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