000			02141nam a2200289Ia 4500
001			343691
003			0000000000
005			20211104091449.0
008			170511t20182018caua b 001 0 eng d
010			_a2017942214
020			_a9781473916364
040			_erda
050			_aQA 279.5 _b.L172 2018
100			_aLambert, Ben, _9127326
245		2	_aA student's guide to Bayesian statistics / _cBen Lambert.
264			_aLos Angeles : _bSAGE, _c2018
300			_axx, 498 pages : _billustrations _c25 cm.
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
338			_avolume _2rdacarrier
504			_aIncludes bibliographical references (pages 489-491) and index.
505			_aAn introduction to Bayesian inference -- Understanding the Bayesian formula -- Analytic Bayesian methods -- A practical guide to doing real-life Bayesian analysis: Computational Bayes -- Hierarchical models and regression.
520			_aSupported by a wealth of learning features, exercises, and visual elements as well as online video tutorials and interactive simulations, this book is the first student-focused introduction to Bayesian statistics. Without sacrificing technical integrity for the sake of simplicity, the author draws upon accessible, student-friendly language to provide approachable instruction perfectly aimed at statistics and Bayesian newcomers. Through a logical structure that introduces and builds upon key concepts in a gradual way and slowly acclimatizes students to using R and Stan software, the book covers: An introduction to probability and Bayesian inference, Understanding Bayes' rule, Nuts and bolts of Bayesian analytic methods, Computational Bayes and real-world Bayesian analysis, Regression analysis and hierarchical methods. This unique guide will help students develop the statistical confidence and skills to put the Bayesian formula into practice, from the basic concepts of statistical inference to complex applications of analyses. --
650			_aBayesian statistical decision theory. _987121
942			_cCIR
999			_c90972 _d90972