Bayesian Essentials with R

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Title:  Bayesian Essentials with R
https://bbs.pinggu.org/thread-2748197-1-1.html
Volume:  
Author(s): Jean-Michel Marin, Christian P. Robert (auth.)
Series: Springer Texts in Statistics Periodical:  
Publisher: Springer New York City:  
Year: 2014 Edition:  
Language: English Pages: 305
ISBN: 978-1-4614-8686-2, 978-1-4614-8687-9 ID: 1038541
Time added: 2013-11-01 18:17:18 Time modified: 2013-11-02 12:27:23
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Size: 7 MB (7776475 bytes) Extension: pdf
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Desr. old vers.: 2013-11-01 18:17:18  2013-11-02 12:27:23 Edit record: Librarian libgen.org
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     10.1007/978-1-4614-8687-9   

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This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications. Readers are empowered to participate in the real-life data analysis situations depicted here from the beginning. The stakes are high and the reader determines the outcome. 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 towards an effective programming of the methods given in the book. In particular, all R codes are discussed with enough detail to make them readily understandable and expandable. This works in conjunction with the bayess package.

Bayesian Essentials with R can be used as a textbook at both undergraduate and graduate levels, as exemplified by courses given at Université Paris Dauphine (France), University of Canterbury (New Zealand), and University of British Columbia (Canada). It is particularly useful with students in professional degree programs and scientists to analyze data the Bayesian way. The text will also enhance introductory courses on Bayesian statistics. Prerequisites for the book are an undergraduate background in probability and statistics, if not in Bayesian statistics. A strength of the text is the noteworthy emphasis on the role of models in statistical analysis.

This is the new, fully-revised edition to the book Bayesian Core: A Practical Approach to Computational Bayesian Statistics.

Table of contents :
Content:
Front Matter....Pages i-xiv
User’s Manual....Pages 1-23
Normal Models....Pages 25-64
Regression and Variable Selection....Pages 65-101
Generalized Linear Models....Pages 103-138
Capture–Recapture Experiments....Pages 139-171
Mixture Models....Pages 173-207
Time Series....Pages 209-250
Image Analysis....Pages 251-283
Back Matter....Pages 285-296

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关键词：Essentials Essential Bayesian Bayes Essen Christian English Series Robert 2014

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