000			03239nam a22003737a 4500
001			sulb0078782
003			BD-SySUS
005			20230822144019.0
008			230822s2013 nyua b 001 0 eng d
020			_a9781489997043
040			_aIND _beng _cIND _dYDXCP _dBTCTA _dUKMGB _dBWX _dMUU _dOCLCF _dOCLCQ _dBEDGE _dDLC
082	0	4	_a300.1 _222 _bCOA
100	1		_aCowles, Mary Kathryn. _963287
245	1	0	_aApplied Bayesian statistics : _bwith R and OpenBUGS examples / _cMary Kathryn Cowles.
260			_aNew York : _bSpringer, _c�2013.
300			_axiv, 232 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 227-229) and index.
505	0		_a1. What is Bayesian statistics? -- 2. Review of probability -- 3. Introduction to one-parameter models : estimating a population proportion -- 4. Inference for a population proportion -- 5. Special considerations in Bayesian inference -- 6. Other one-parameter models and their conjugate priors -- 7. More realism please : introduction to multiparameter models -- 8. Fitting more complex Bayesian models : Markov chain Monte Carlo -- 9. Hierarchical models and more on convergence assessment -- 10. Regression on hierarchical regression models -- 11. Model comparison, model checking, and hypothesis testing.
520	3		_aThis book is based on over a dozen years teaching a Bayesian Statistics course. The material presented here has been used by students of different levels and disciplines, including advanced undergraduates studying Mathematics and Statistics and students in graduate programs in Statistics, Biostatistics, Engineering, Economics, Marketing, Pharmacy, and Psychology. The goal of the book is to impart the basics of designing and carrying out Bayesian analyses, and interpreting and communicating the results. In addition, readers will learn to use the predominant software for Bayesian model-fitting, R and OpenBUGS. The practical approach this book takes will help students of all levels to build understanding of the concepts and procedures required to answer real questions by performing Bayesian analysis of real data. Topics covered include comparing and contrasting Bayesian and classical methods, specifying hierarchical models, and assessing Markov chain Monte Carlo output. -- _cP.4 of cover.
650		0	_aBayesian statistical decision theory. _963288
650		7	_aM�ethodes statistiques. _2eclas _963289
650		7	_aAnalyse statistique. _2eclas _963290
650		7	_aBayesian statistical decision theory. _2fast _0(OCoLC)fst00829019 _963291
830		0	_aSpringer texts in statistics. _963292
856	4	1	_3Ebook Library _uhttp://public.eblib.com/choice/publicfullrecord.aspx?p=1081980
856	4	2	_3Contributor biographical information _uhttp://www.loc.gov/catdir/enhancements/fy1509/2012951150-b.html
856	4	2	_3Publisher description _uhttp://www.loc.gov/catdir/enhancements/fy1509/2012951150-d.html
856	4	1	_3Table of contents only _uhttp://www.loc.gov/catdir/enhancements/fy1509/2012951150-t.html
942			_2ddc _cBK
999			_c84936 _d84936