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*008131007t20142014xxua   | b   |0|1|0 eng|c
*020  $a978-1-4398-4095-5$c67,53$qsidottu
*035  $a(FI-MELINDA)006425834
*0410 $aeng
*072 7$a04.2$2kkaa
*080  $a519.226.3
*080  $a303.7
*080  $a519.23
*080  $a51
*08200$a519.5/42$223
*100  $aGelman, Andrew
*24500$aBayesian data analysis /$cAndrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin.
*250  $aThird edition.
*260  $aBoca Raton$bCRC Press$c2014
*264 1$aBoca Raton :$bCRC Press,$c[2014]
*264 4$c©2014
*300  $axiv, 661 sivua :$bkuvitettu
*336  $ateksti$btxt$2rdacontent
*337  $akäytettävissä ilman laitetta$bn$2rdamedia
*338  $anide$bnc$2rdacarrier
*4901 $aChapman & Hall/CRC texts in statistical science series
*500  $aPrevious ed.: 2003.
*500  $aMat.-Luonnont. tdk
*500  $aMAT22005 Bayesian inference
*504  $aSisältää bibliografisia lähdeviitteitä ja hakemiston.
*520  $a"This book is intended to have three roles and to serve three associated audiences: an introductory text on Bayesian inference starting from first principles, a graduate text on effective current approaches to Bayesian modeling and computation in statistics and related fields, and a handbook of Bayesian methods in applied statistics for general users of and researchers in applied statistics. Although introductory in its early sections, the book is definitely not elementary in the sense of a first text in statistics. The mathematics used in our book is basic probability and statistics, elementary calculus, and linear algebra. A review of probability notation is given in Chapter 1 along with a more detailed list of topics assumed to have been studied. The practical orientation of the book means that the reader's previous experience in probability, statistics, and linear algebra should ideally have included strong computational components. To write an introductory text alone would leave many readers with only a taste of the conceptual elements but no guidance for venturing into genuine practical applications, beyond those where Bayesian methods agree essentially with standard non-Bayesian analyses. On the other hand, we feel it would be a mistake to present the advanced methods without first introducing the basic concepts from our data-analytic perspective. Furthermore, due to the nature of applied statistics, a text on current Bayesian methodology would be incomplete without a variety of worked examples drawn from real applications. To avoid cluttering the main narrative, there are bibliographic notes at the end of each chapter and references at the end of the book"--$cProvided by publisher.
*530  $aJulkaistu myös verkkoaineistona.
*650 0$aBayesian statistical decision theory.
*650 7$atilastotiede$2yso/fin$0http://www.yso.fi/onto/yso/p3591
*650 7$amatemaattinen tilastotiede$2yso/fin$0http://www.yso.fi/onto/yso/p3590
*650 7$abayesilainen menetelmä$2yso/fin$0http://www.yso.fi/onto/yso/p17803
*650 7$atilastomenetelmät$2yso/fin$0http://www.yso.fi/onto/yso/p3127
*650 7$astatistik (vetenskaper)$2yso/swe$0http://www.yso.fi/onto/yso/p3591
*650 7$amatematisk statistik$2yso/swe$0http://www.yso.fi/onto/yso/p3590
*650 7$abayesiska metoder$2yso/swe$0http://www.yso.fi/onto/yso/p17803
*650 7$astatistiska metoder$2yso/swe$0http://www.yso.fi/onto/yso/p3127
*650 7$atilastotiede$2helecon
*650 7$amatematiikka$2helecon
*650 7$astatistical science$2helecon
*650 7$amathematics$2helecon
*653 0$aMATHEMATICS / Probability & Statistics / General.
*7001 $ekirjoittaja.
*7001 $ekirjoittaja.
*7001 $aStern, Hal S.,$ekirjoittaja.
*7001 $aDunson, David B.,$ekirjoittaja.
*7001 $aVehtari, Aki,$ekirjoittaja.
*7001 $aRubin, Donald B.,$ekirjoittaja.
*77608$iVerkkoaineisto:$z9781439898208
*830 0$aChapman & Hall/CRC texts in statistical science.
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Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.