Bayesian Statistics: An Introduction - Couverture souple

Lee, Peter M.

9780470689202: Bayesian Statistics: An Introduction

Couverture souple

ISBN 10 : 047068920X ISBN 13 : 9780470689202

Editeur : Wiley-Blackwell, 2009

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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.

Les informations fournies dans la section � Synopsis � peuvent faire r�f�rence � une autre �dition de ce titre.

Pr�sentation de l'�diteur

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.

Pr�sentation de l'�diteur

Among statisticians the Bayesian approach continues to gain adherents and so it is appropriate that Peter Lee has updated and expanded his successful introduction to the subject in this second edition. An additional chapter has been added which deals with hierarchical methods in Bayesian statistics and gives a fuller treatment of empirical Bayes methods. Another chapter deals with real numerical methods, especially the EM algorithm and Gibbs sampling. Other alterations have been made throughout the text, including the addition of a brief description of Bayes linear methods, and the number of exercises has been considered increased.

Les informations fournies dans la section � A propos du livre � peuvent faire r�f�rence � une autre �dition de ce titre.