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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:
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.
Informazioni sull?autore: Peter Lee, Department of Mathematics & Formerly Provost of Wentworth College, University of York.
Titolo: Bayesian Statistics An Introduction
Casa editrice: John Wiley & Sons, Incorporated
Data di pubblicazione: 2012
Legatura: Brossura
Condizione: Very Good
Edizione: quarta edizione
Da: Andrew Isles Natural History Books, Prahran, VIC, Australia
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. Codice articolo 34924
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