Bayesian core : a practical approach to computational Bayesian statistics
Jean-Michel Marin, Christian P. Robert
"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.- Generalised linear models.- Capture-recapture experiments.- Mixture models.- Dynamic models.- Image analysis
User's manual.- Normal models.- Regression and variable selection.- Generalised linear models.- Capture-recapture experiments.- Mixture models.- Dynamic models.- Image analysis
年:
2007
出版:
English
出版社:
Springer
语言:
english
页:
1
ISBN 10:
0387389830
ISBN 13:
9780387389837
系列:
Springer texts in statistics
文件:
PDF, 9.69 MB
IPFS:
,
english, 2007
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