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001			978-1-4614-8687-9
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008			131028s2014 xxu| s |||| 0|eng d
020			_a9781461486879 _9978-1-4614-8687-9
024	7		_a10.1007/978-1-4614-8687-9 _2doi
050		4	_aQA276-280
072		7	_aUFM _2bicssc
072		7	_aCOM077000 _2bisacsh
082	0	4	_a519.5 _223
100	1		_aMarin, Jean-Michel. _eauthor.
245	1	0	_aBayesian Essentials with R _h[electronic resource] / _cby Jean-Michel Marin, Christian P. Robert.
250			_a2nd ed. 2014.
264		1	_aNew York, NY : _bSpringer New York : _bImprint: Springer, _c2014.
300			_aXIV, 296 p. 75 illus., 38 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringer Texts in Statistics, _x1431-875X
505	0		_aUser's Manual -- Normal Models -- Regression and Variable Selection -- Generalized Linear Models -- Capture-Recapture Experiments -- Mixture Models -- Time Series -- Image Analysis -- References -- Index.
520			_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. This works in conjunction with the bayess package. Bayesian Essentials with R can be used as a textbook at both undergraduate and graduate levels, as exemplified by courses given at Université Paris Dauphine (France), University of Canterbury (New Zealand), and University of British Columbia (Canada). It is particularly useful with students in professional degree programs and scientists to analyze data the Bayesian way. The text will also enhance introductory courses on Bayesian statistics. Prerequisites for the book are an undergraduate background in probability and statistics, if not in Bayesian statistics. A strength of the text is the noteworthy emphasis on the role of models in statistical analysis. This is the new, fully-revised edition to the book Bayesian Core: A Practical Approach to Computational Bayesian Statistics.
650		0	_aStatistics.
650		0	_aMathematical statistics.
650	1	4	_aStatistics.
650	2	4	_aStatistics and Computing/Statistics Programs.
650	2	4	_aStatistical Theory and Methods.
700	1		_aRobert, Christian P. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781461486862
830		0	_aSpringer Texts in Statistics, _x1431-875X
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4614-8687-9
912			_aZDB-2-SMA
999			_c92223 _d92223