02130cam a22002895i 450000500170000000800410001701000170005802000150007502000180009004000220010808200200013010000240015024500740017425000200024830000510026849000340031950400670035350501860042052009540060665000420156065000340160270000340163694200070167090600450167799900190172295200990174120140709113213.0130913s2014 nyua b 001 0 eng c a 2013950378 a1461486866 a9781461486862 aYDXCPbengcYDXCP a000SA.161bM3371 aMarin, Jean-Michel,10aBayesian essentials with R /cJean-Michel Marin, Christian P. Robert. aSecond edition. axiv, 296 pages :billustrations (some color) ;1 aSpringer Texts in Statistics, aIncludes bibliographical references (pages 287-290) and index.0 aUser's manual -- Normal models -- Regression and variable selection -- Generalized linear models -- Capture-recapture experiments -- Mixture models -- Time series -- Image analysis. aThis 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. 0aBayesian statistical decision theory. 0aR (Computer program language)1 aRobert, Christian P.,d1961-, cBK a0bibccpccadapd2encipf20gy-gencatlg c415824d415824 00102ddc4070aMAINbMAINd2014-07-09l1o000SA.161 M337p134962r2015-07-06s2015-07-03yBK