000			02839cam a2200301 i 4500
001			vtls000347163
003			UG-KaMUL
005			20250614173135.0
010			_a 2013-039507
020			_a 9781439840955 (hardback)
039			_a 202308041105 _b 992 _y 202011061551 _z 992
040			_a DLC _b eng _c DLC _e rda _d DLC _d UG-KaMUL
082			_a 519.5/42 _2 23
092			_a 519.542 BAY
100			_a Gelman, Andrew.
245			_a Bayesian data analysis / _c Andrew Gelman, John B. Carlin, Hal S. St ern, David B. Dunson, Aki Vehtari, Donald B. Rubin.
250			_a 3rd edition.
260			_a Boca Raton : _b CRC Press, _c 2014.
300			_a xiv, 667 p. : _b ill. ; _c 27 cm.
490			_a Chapman & Hall/CRC texts in statistical science
504			_a Includes bibliographical references (pages 607-639) and indexes.
520			_a "Preface This book is intended to have three roles and to serve thre e associated audiences: an introductory text on Bayesian inference star ting from first principles, a graduate text on effective current approa ches to Bayesian modeling and computation in statistics and related fie lds, and a handbook of Bayesian methods in applied statistics for gener al users of and researchers in applied statistics. Although introductor y in its early sections, the book is definitely not elementary in the s ense of a first text in statistics. The mathematics used in our book is basic probability and statistics, elementary calculus, and linear alge bra. A review of probability notation is given in Chapter 1 along with a more detailed list of topics assumed to have been studied. The practi cal orientation of the book means that the reader's previous experience in probability, statistics, and linear algebra should ideally have inc luded strong computational components. To write an introductory text al one would leave many readers with only a taste of the conceptual elemen ts but no guidance for venturing into genuine practical applications, b eyond 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 concep ts from our data-analytic perspective. Furthermore, due to the nature o f applied statistics, a text on current Bayesian methodology would be i ncomplete without a variety of worked examples drawn from real applicat ions. To avoid cluttering the main narrative, there are bibliographic n otes at the end of each chapter and references at the end of the book"- - _c Provided by publisher.
650			_a Bayesian statistical decision theory.
942			_2ddc _cBK
999			_c636508 _d636508