03364cam 22004098i 45000010005000000030009000050050017000140080041000310100017000720200029000890200015001180350021001330400033001540410008001870500023001950820022002180840023002401000026002632450122002892500019004112630009004303000014004394900052004535040053005055050010005585201707005685260007022755900016022826500048022986500069023468560095024158560112025109420017026229990015026399520153026549520147028077364BD-DhEWU20140605020005.0140205s2014 flu g b 001 0 eng d a 2013039507 a9781439840955 (hardback) a1439840954 a(OCoLC)859253474 aDLCbengcDLCerdadBD-DhEWU aeng00aQA279.5b.G45 201400a519.542bBAY 2014 aMAT0290002bisacsh1 aGelman, Andrew,9349410aBayesian data analysis /cAndrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin. aThird edition. a1111 apages cm.0 aChapman & Hall/CRC texts in statistical science aIncludes bibliographical references and indexes. tTOCa 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"-- aAS aTahur Ahmed 0aBayesian statistical decision theory.92787 7aMATHEMATICS / Probability & Statistics / General.2bisacsh92343423Cover imageuhttp://images.tandf.co.uk/common/jackets/websmall/978143984/9781439840955.jpg423WorldCat Detailsuhttp://www.worldcat.org/title/bayesian-data-analysis/oclc/859253474&referer=brief_results 2ddccTEXT01 c7364d7364 00102ddc40718NFICaEWUbEWUcREVd2014-02-05eTrim Educationg4690.00l1o519.542 BAY 2014p25719r2014-06-17s2014-06-04tC-1w2014-02-10yTEXT 00102ddc40718NFICaEWUbEWUcREVd2014-06-29eBangaldesh Photostatg1273.00l0o519.542 BAY 2014p26045r2014-07-07tC-2w2014-07-07yTEXT