Bayesian Statistics: An Introduction

by Peter M. Lee

Bayesian Statistics is the school of thought that uses all information surrounding the likelihood of an event rather than just that collected experimentally. Among statisticians the Bayesian approach continues to gain adherents and this new edition of Peter Lee’s well-established introduction maintains the clarity of exposition and use of examples for which this 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 (Bayesian Inference Using Gibbs Sampling) as this is now the standard computational tool for such numerical work. Other alterations include new material on generalized linear modelling and Bernardo’s theory of reference points.

Genres: Mathematics, Reference

351 pages, Paperback

First published January 1, 1900

Reviews

[Otto Hahaa · Rating 2 out of 5]

I have had the second edition on my shelf for more than ten years. From time to time I pick it up and start reading. But every time there is a point where I just have to stop, because I cannot understand anything anymore. This point keeps creeping forwards, so I am not completely hopeless. Unfortunately, the world has gone forward, and the second edition is sort of outdated. More modern books would put MCMC methods more in the centre, not just as an after thought at the end of the book. So, I must find another Bayesian book I should try to read for the next ten years…

[Tarek Amr · Rating 2 out of 5]

Is it me or the book is very hard to grasp. Every time I pick it to read about a concept there, I get lost in mathematical notation with no clear explanation.