000			03041cam a2200337 i 4500
001			17902843
003			KWUST
005			20240212150320.0
008			130930s2014 flua b 001 0 eng
010			_a 2013039507
020			_a9781439840955 (hardback)
040			_aLCC _beng _cKWUST
050	0	0	_aQA 279.5 _b.G45 2014
100	1		_aGelman, Andrew, _eauthor.
245	1	0	_aBayesian data analysis / _cAndrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin.
250			_aThird edition.
264		1	_aBoca Raton : _bCRC Press, _c2014.
300			_axiv, 661 pages : _billustrations ; _c27 cm.
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
338			_avolume _2rdacarrier
490	0		_aChapman & Hall/CRC texts in statistical science
504			_aIncludes bibliographical references (pages 607-639) and indexes.
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"-- _cProvided by publisher.
650		0	_aBayesian statistical decision theory.
650		7	_aMATHEMATICS / Probability & Statistics / General. _2bisacsh
856	4	2	_3Cover image _uhttp://images.tandf.co.uk/common/jackets/websmall/978143984/9781439840955.jpg
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2lcc _cBK
999			_c2662 _d2662