University of Sheffield

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A Bayesian Statistics Reading List

I am frequently asked to recommend books on Bayesian Statistics. Here are a few suggestions, with notes. Note that this page was written in 2004. There are surely many newer and excellent books, but I still think the ones listed here are worth studying.

Preparatory books

In this category I include some recommendations that are preparatory reading. A very fine book by Dennis Lindley deals with decision making, in very much a Bayesian way using personal probability. And my 1988 book is an introduction to personal probability, which I believe it is important to understand if you are interested in learning about Bayesian Statistics.

Neither book assumes knowledge of mathematics above a very elementary level.

Introductory books

The best introductory text at a truly introductory level, with only the most elementary mathematics required, is by Don Berry. It has a slant to medical applications. The best text that is also introductory but assumes college level mathematics is by Peter Lee.

More advanced texts

At an intermediate level, there is quite a nice, readable but concise book by Helio Migon and Dani Gamerman. The next two books concentrate on how to develop models and computations for the practical application of Bayesian methods. The coverage of the two books overlaps somewhat. The last two books, by Jose Bernardo and Adrian Smith, and by Jon Forster and myself, are for people wishing to learn Bayesian Statistics in depth. Both assume good mathematical knowledge and some previous introduction to statistical theory.
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Updated: 11 January 2017
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