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dc.contributor.authorMarin, Jean-Michel
dc.contributor.authorRobert, Christian P.
dc.date.issued2014
dc.identifier.isbn978-1-4614-8687-9
dc.identifier.urihttps://thuvienso.hoasen.edu.vn/handle/123456789/10498
dc.descriptionxiv, 296 p. : ill.
dc.description.abstractThis 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.
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofseriesSpringer Texts in Statistics
dc.subjectStatistics
dc.subject.otherBayesian statistical decision theory
dc.subject.otherR (Computer program language)
dc.titleBayesian essentials with R
dc.typeBook


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