Record Display for the EPA National Library Catalog


Main Title Bayesian core : a practical approach to computational Bayesian statistics /
Author Marin, Jean-Michel.
Other Authors
Author Title of a Work
Robert, Christian P.,
Publisher Springer,
Year Published 2007
OCLC Number 83599756
ISBN 9780387389790; 0387389792; 9780387389837; 0387389830
Subjects Bayesian statistical decision theory--Textbooks. ; Bayes-Entscheidungstheorie ; Bayes-Verfahren ; Numerisches Verfahren ; Statistisches Modell ; Inferãencia bayesiana (inferãencia estatística) ; Datenverarbeitung ; Bayes-Entscheidungstheorie.--(DE-588)4144220-9 ; Bayes-Verfahren.--(DE-588)4204326-8 ; Numerisches Verfahren.--(DE-588)4128130-5 ; Statistisches Modell.--(DE-588)4121722-6 ; Inferãencia bayesiana (inferãencia estatâistica)
Internet Access
Description Access URL
Table of contents
Table of contents
Table of contents
Table of contents
Publisher description
Kapitel 1
Vorwort 1
Library Call Number Additional Info Location Last
EKBM  QA279.5.M357 2007 Research Triangle Park Library/RTP, NC 11/23/2009
Collation xiii, 255 pages : illustrations ; 25 cm.
Includes bibliographical references (pages 247-249) and index.
Contents Notes
"This Bayesian modeling book is intended for practitioners and applied statisticians looking for a self-contained entry to computational Bayesian statistics. Focusing on standard statistical models and backed up by discussed real datasets available from the book's Web site, it provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical justifications. Special attention is paid to the derivation of prior distributions in each case, and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader toward an effective programming of the methods given in the book. While R programs are provided on the book's Web site and R hints are given in the computational sections of the book, Bayesian Core: A Practical Approach to Computational Bayesian Statistics requires no knowledge of the R language, and it can be read and used with any other programming language."--Jacket. User's manual -- Normal models -- Regression and variable selection -- Generalized linear models -- Capture-recapture experiments -- Mixture models -- Dynamic models -- Image analysis.