Bayesian statistics: an introduction.

Lee, Peter M.

ISBN 10: 1118332571 / ISBN 13: 9781118332573

Edité par New York: John Wiley and Sons, (2012, 2012

A propos de cet article

fourth edition).Octavo,462 pp.,softcover. Now in its fourth edition, this book looks at recent techniques such as variational methods, Bayesian importance sampling, approximate Bayesian computation and Reversible Jump Markov Chain Monte Carlo (RJMCMC). This book provides a concise account of the way in which the Bayesian approach to statistics develops as well as how it contrasts with the conventional approach. The theory is built up step by step, and important notions such as sufficiency are brought out of a discussion of the salient features of specific examples. This edition also includes expanded coverage of Gibbs sampling, including mor numerical examples and treatments of OpenBUGS, R2WinBUGS and R2OpenBUGS. This book is best suited for use as a main text in courses on Bayesian statistics for third and fourth year undergraduates and postgraduate students. N° de réf. du vendeur 34924

Détails bibliographiques

Titre : Bayesian statistics: an introduction.

Éditeur : New York: John Wiley and Sons, (2012

Date d'édition : 2012

Reliure : Couverture souple

Edition : 4ème Édition

A propos de ce titre

Synopsis :

Bayesian Statistics is the school of thought that combines prior beliefs with the likelihood of a hypothesis to arrive at posterior beliefs. The first edition of Peter Lee's book appeared in 1989, but the subject has moved ever onwards, with increasing emphasis on Monte Carlo based techniques. This new fourth edition looks at recent techniques such as variational methods, Bayesian importance sampling, approximate Bayesian computation and Reversible Jump Markov Chain Monte Carlo (RJMCMC), providing a concise account of the way in which the Bayesian approach to statistics develops as well as how it contrasts with the conventional approach. The theory is built up step by step, and important notions such as sufficiency are brought out of a discussion of the salient features of specific examples. This edition: Includes expanded coverage of Gibbs sampling, including more numerical examples and treatments of OpenBUGS, R2WinBUGS and R2OpenBUGS. Presents significant new material on recent techniques such as Bayesian importance sampling, variational Bayes, Approximate Bayesian Computation (ABC) and Reversible Jump Markov Chain Monte Carlo (RJMCMC).Provides extensive examples throughout the book to complement the theory presented. Accompanied by a supporting website featuring new material and solutions. More and more students are realizing that they need to learn Bayesian statistics to meet their academic and professional goals. This book is best suited for use as a main text in courses on Bayesian statistics for third and fourth year undergraduates and postgraduate students.

Revue de presse:

As a lifelong non–statistician and sporadic user of statistics, I have not come across another advanced statistics book (as I would characterize this one) that offers so much to the non–expert and, I ll bet, to the expert as well. The book has my highest recommendation.   (Computing Reviews, 7 January 2013)