000			03114cam a22004937i 4500
001			17884899
003			OSt
005			20210930200147.0
008			130913s2014 nyua b 001 0 eng c
010			_a 2013950378
020			_a9781461486862 (hbk)
020			_a1461486866 (hbk)
020			_z1461486874 (ebk.)
020			_z9781461486879 (ebk.)
035			_a(OCoLC)ocn859186504
040			_aYDXCP _beng _cYDXCP _erda _dBTCTA _dCDX _dDGU _dIXA _dMNW _dDLC
042			_apcc
050	0	0	_aQA279.5 _b.M358 2014
100	1		_aMarin, Jean-Michel, _eauthor.
245	1	0	_aBayesian essentials with R / _cJean-Michel Marin, Christian P. Robert.
250			_aSecond edition.
264		1	_aNew York : _bSpringer, _c[2014]
300			_axiv, 296 pages : _billustrations (some color) ; _c24 cm.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
490	1		_aSpringer Texts in Statistics, _x1431-875X
504			_aIncludes bibliographical references (pages 287-290) and index.
505	0		_aUser's manual -- Normal models -- Regression and variable selection -- Generalized linear models -- Capture-recapture experiments -- Mixture models -- Time series -- Image analysis.
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.
530			_aAlso available online.
650		0	_aBayesian statistical decision theory.
650		0	_aR (Computer program language)
700	1		_aRobert, Christian P., _d1961-, _eauthor.
776	0	8	_iAvailable online as: _aMarin, Jean-Michel. _tBayesian essentials with R. _bSecond edition. _dNew York : Springer, 2014 _z9781461486879 _w(OCoLC)864180801
830		0	_aSpringer texts in statistics.
856	4	2	_3Contributor biographical information _uhttp://www.loc.gov/catdir/enhancements/fy1410/2013950378-b.html
856	4	1	_3Table of contents only _uhttp://www.loc.gov/catdir/enhancements/fy1410/2013950378-t.html
856	4	2	_3Publisher description _uhttp://www.loc.gov/catdir/enhancements/fy1410/2013950378-d.html
906			_a7 _bcbc _cpccadap _d2 _encip _f20 _gy-gencatlg
942			_2lcc _cBK
999			_c6295 _d6295