000			03888cam a2200517Ki 4500
001			9780429202292
003			FlBoTFG
005			20220509193054.0
006			m o d
007			cr cnu---unuuu
008			190415s2019 flu o 000 0 eng d
040			_aOCoLC-P _beng _erda _epn _cOCoLC-P
020			_a9780429202292 _q(electronic bk.)
020			_a0429202296 _q(electronic bk.)
020			_a9780429517778 _q(electronic bk. : Mobipocket)
020			_a0429517777 _q(electronic bk. : Mobipocket)
020			_a9780429510915 _q(electronic bk. : PDF)
020			_a0429510918 _q(electronic bk. : PDF)
020			_a9780429514340 _q(electronic bk. : EPUB)
020			_a0429514344 _q(electronic bk. : EPUB)
020			_z9780815378648
020			_z0815378645
035			_a(OCoLC)1097183939
035			_a(OCoLC-P)1097183939
050		4	_aQA279.5 _b.R445 2019eb
072		7	_aMAT _x003000 _2bisacsh
072		7	_aMAT _x029000 _2bisacsh
072		7	_aPBT _2bicssc
082	0	4	_a519.5/42 _223
100	1		_aReich, Brian J. _q(Brian James), _eauthor.
245	1	0	_aBayesian statistical methods / _cBrian J. Reich, Sujit K. Ghosh.
264		1	_aBoca Raton : _bCRC Press, Taylor & Francis Group, _c2019.
300			_a1 online resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aChapman & Hall/CRC texts in statistical science series
520			_aBayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. This book focuses on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized linear models (GLM). The authors include many examples with complete R code and comparisons with analogous frequentist procedures. In addition to the basic concepts of Bayesian inferential methods, the book covers many general topics: Advice on selecting prior distributions Computational methods including Markov chain Monte Carlo (MCMC) Model-comparison and goodness-of-fit measures, including sensitivity to priors Frequentist properties of Bayesian methods Case studies covering advanced topics illustrate the flexibility of the Bayesian approach: Semiparametric regression Handling of missing data using predictive distributions Priors for high-dimensional regression models Computational techniques for large datasets Spatial data analysis The advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code, motivating data sets, and complete data analyses are available on the book's website. Brian J. Reich, Associate Professor of Statistics at North Carolina State University, is currently the editor-in-chief of the Journal of Agricultural, Biological, and Environmental Statistics and was awarded the LeRoy & Elva Martin Teaching Award. Sujit K. Ghosh, Professor of Statistics at North Carolina State University, has over 22 years of research and teaching experience in conducting Bayesian analyses, received the Cavell Brownie mentoring award, and served as the Deputy Director at the Statistical and Applied Mathematical Sciences Institute.
588			_aOCLC-licensed vendor bibliographic record.
650		0	_aBayesian statistical decision theory _vProblems, exercises, etc.
650		0	_aMathematical analysis _vProblems, exercises, etc.
650		7	_aMATHEMATICS / Applied _2bisacsh
650		7	_aMATHEMATICS / Probability & Statistics / General _2bisacsh
700	1		_aGhosh, Sujit K., _d1970- _eauthor.
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9780429202292
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999			_c129282 _d129282