000			01544nam a22002297a 4500
005			20240429091123.0
008			240122b2014 |||||||| |||| 00| 0 eng d
020			_a9781461486862
040			_aB-IKIAM
041			_aeng
082			_a519.542 _bM337
100			_92503 _aMarin, Jean-Michel
245			_aBayesian Essentials with R _cJean-Michel Marin; Christian P. Robert
250			_a2° ed.
260			_aNew York, _bSpringer _c2014.
300			_axiv, 296 pág.; _bFiguras; _c24 x 16 cm.
505			_aUser's Manual -- Normal Models -- Regression and Variable Selection -- Generalized Linear Models -- Capture-Recapture Experiments -- Mixture Models -- Time Serie -- Image Analysis
520			_aThe 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 stake 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.
700			_92504 _aRobert Christian P.
856			_6http://www.springer.com/series/417
942			_2ddc _aB-IKIAM _b26-04-2024 _cBK _zR.A.
999			_c2237 _d2237