000			02693cam a2200349 i 4500
003			OSt
005			20260717135754.0
008			130930s2014 flua b 001 0 eng
020			_a9781439840955
040			_cSTCPL
041			_aENG
082	0	0	_a519.542 _bAND-B
100	1		_aGelman, Andrew
100	1		_aCarlin, John B.
100	1		_aStern, Hal S.
100	1		_aDunson, David B.
100	1		_aVehtari, Aki
100	1		_aRubin, Donald B.
245	1	0	_aBayesian data analysis / _cby Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin.
250			_a3rd ed.
260			_aBoca Raton : _bCRC Press, Taylor & Francis Group, _c2026.
300			_axiv, 667 p. _bPB
490	0		_aTexts in statistical science
520			_a"Preface:- This book is intended to have three roles and to serve three associated audiences: an introductory text on Bayesian inference starting from first principles, a graduate text on effective current approaches to Bayesian modeling and computation in statistics and related fields, and a handbook of Bayesian methods in applied statistics for general users of and researchers in applied statistics. Although introductory in its early sections, the book is definitely not elementary in the sense of a first text in statistics. The mathematics used in our book is basic probability and statistics, elementary calculus, and linear algebra. A review of probability notation is given in Chapter 1 along with a more detailed list of topics assumed to have been studied. The practical orientation of the book means that the reader's previous experience in probability, statistics, and linear algebra should ideally have included strong computational components. To write an introductory text alone would leave many readers with only a taste of the conceptual elements but no guidance for venturing into genuine practical applications, beyond those where Bayesian methods agree essentially with standard non-Bayesian analyses. On the other hand, we feel it would be a mistake to present the advanced methods without first introducing the basic concepts from our data-analytic perspective. Furthermore, due to the nature of applied statistics, a text on current Bayesian methodology would be incomplete without a variety of worked examples drawn from real applications. To avoid cluttering the main narrative, there are bibliographic notes at the end of each chapter and references at the end of the book"--
650		0	_aMathematics
650		0	_aStatistics
650		0	_aProbability & Statistics
650		0	_aBayesian statistical decision theory
942			_cBK
942			_2ddc
942			_2ddc
999			_c87619 _d87619