Bayesian essentials with R / Jean-Michel Marin, Christian P. Robert - Second edition Second edition - 1 online resource (xiv, 296 pages) : illustrations (some color). - - Springer Texts in Statistics, 1431-875X . - Springer texts in statistics. .

Includes bibliographical references and index

User's Manual -- Normal Models -- Regression and Variable Selection -- Generalized Linear Models -- Capture-Recapture Experiments -- Mixture Models -- Time Series -- Image Analysis

Available to OhioLINK libraries

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

9781461486879 (electronic bk.) 1461486874 (electronic bk.) 1461486866 (print) 9781461486862 (print)

10.1007/978-1-4614-8687-9 doi

Bayesian statistical decision theory.

R (Computer program language)

Statistics.

Statistics and Computing/Statistics Programs.

Statistical Theory and Methods.

Electronic books.

QA279.5

519.5/42