Synopsis
Statistics is concerned with investigating the degree of confidence we can have in various hypotheses. The Bayesian approach is distinguished by giving each hypothesis a probability and then modifying it in the light of the experimental data. This is controversial because for a new theory with no data available, an element of guesswork has to be involved. The author presents the ideas behind Bayesian statistics at a level suitable for advanced undergraduate or postgraduate students. The discrepancies between the conclusions of Bayesian and classical statistics are highlighted.
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From the Publisher
This new edition of Lee's popular book introduces the Bayesian philosophy of statistics. It has been completely updated and features new chapters on Gibbs sampling and hierarchical methods and more exercises.
From the Back Cover
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.
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Bayesian Statistics: An Introduction
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Oxford University Press, New York, NY, USA, 1989
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