Bayesian essentials with R

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...

Description complète

Enregistré dans:
Détails bibliographiques
Auteurs principaux : Marin Jean-Michel (Auteur), Robert Christian P. (Auteur)
Format : Livre
Langue : anglais
Titre complet : Bayesian essentials with R / Jean-Michel Marin, Christian P. Robert
Édition : Second edition
Publié : New York : Springer , cop. 2014
Description matérielle : 1 vol. (XIV-296 p.)
Collection : Springer texts in statistics advisors George Casella, Stephen Fienberg, Ingram Olkin
Sujets :
LEADER 02633cam a2200469 4500
001 PPN177472758
003 http://www.sudoc.fr/177472758
005 20210518055300.0
010 |a 978-1-4614-8686-2  |b rel. 
010 |a 1-461-48686-6  |b rel. 
035 |a (OCoLC)859186504 
073 1 |a 9781461486862 
100 |a 20140409h20142014k y0frey0103 ba 
101 0 |a eng 
102 |a US 
105 |a a a 001yy 
106 |a r 
181 |6 z01  |c txt  |2 rdacontent 
181 1 |6 z01  |a i#  |b xxxe## 
182 |6 z01  |c n  |2 rdamedia 
182 1 |6 z01  |a n 
200 1 |a Bayesian essentials with R  |f Jean-Michel Marin, Christian P. Robert 
205 |a Second edition 
210 |a New York  |c Springer  |d cop. 2014 
215 |a 1 vol. (XIV-296 p.)  |c ill. en noir et en coul., couv. ill. en coul.  |d 24 cm 
225 2 |a Springer Texts in Statistics  |x 1431-875X 
320 |a Bibliogr. p. 287-290. Index 
330 |a 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 
410 | |0 06999711X  |t Springer texts in statistics  |b Texte imprimé  |f advisors George Casella, Stephen Fienberg, Ingram Olkin  |c New-York  |n Springer  |d 1985- 
606 |3 PPN029753090  |a Statistique bayésienne  |2 rameau 
606 |3 PPN08080859X  |a R  |2 rameau 
676 |a 519.542  |v 23 
680 |a QA279.5 
700 1 |3 PPN060239190  |a Marin  |b Jean-Michel  |f 1974-....  |c mathématicien  |4 070 
701 1 |3 PPN031252753  |a Robert  |b Christian P.  |f 1961-....  |c mathématicien  |4 070 
801 3 |a FR  |b Abes  |c 20171020  |g AFNOR 
915 |5 441092208:639544576  |b 22079 
930 |5 441092208:639544576  |b 441092208  |a 62C588  |j u 
979 |a CCFA 
991 |5 441092208:639544576  |a exemplaire créé automatiquement par l'ABES 
997 |a CCFA  |b 22079  |d CMB  |e BAP  |s d  |c 62C588 
998 |a 850197