LEADER 03579nam0 2200517 i 450 001 VAN00113419 005 20260625075505.250 017 70$2N$a9783319162386 035 40$a1591420883 100 $a20180109d2015 |0itac50 ba 101 $aeng 102 $aCH 105 $a|||| ||||| 181 $ai$b e 182 $ab 183 $acr 200 1 $aBayesian statistics from methods to models and applications$eresearch from BAYSM 2014$fSylvia Frühwirth-Schnatter ... [et al.] editors 210 $a[Cham]$cSpringer$d2015 215 $aXIII, 167 p.$cill.$d24 cm 327 $aThe Second Bayesian Young Statisticians Meeting (BAYSM 2014) and the research presented here facilitate connections among researchers using Bayesian Statistics by providing a forum for the development and exchange of ideas. WU Vienna University of Business and Economics hosted BAYSM 2014 from September 18th to the 19th. The guidance of renowned plenary lecturers and senior discussants is a critical part of the meeting and this volume, which follows publication of contributions from BAYSM 2013. The meeting's scientific program reflected the variety of fields in which Bayesian methods are currently employed or could be introduced in the future. Three brilliant keynote lectures by Chris Holmes (University of Oxford), Christian Robert (Université Paris-Dauphine), and Mike West (Duke University), were complemented by 24 plenary talks covering the major topics Dynamic Models, Applications, Bayesian Nonparametrics, Biostatistics, Bayesian Methods in Economics, and Models and Methods, as well as a lively poster session with 30 contributions. Selected contributions have been drawn from the conference for this book. All contributions in this volume are peer-reviewed and share original research in Bayesian computation, application, and theory. 410 1$1001VAN00102574$12001 $aSpringer proceedings in mathematics & statistics$1210 $aBerlin [etc.]$cSpringer$d2012-$v126 500 1$3VAN00234919$aBayesian statistics from methods to models and applications : research from BAYSM 2014$91522591 606 $a62-XX$xStatistics [MSC 2020]$3VANC022998$2MF 606 $a62C10$xBayesian problems; characterization of Bayes procedures [MSC 2020]$3VANC028328$2MF 606 $a62F15$xBayesian inference [MSC 2020]$3VANC024528$2MF 610 $aApplied Bayesian Statistics$9KW:K 610 $aBayesian Estimation$9KW:K 610 $aBayesian Statistics$9KW:K 610 $aBayesian Statistics Applications$9KW:K 610 $aBayesian Survival Model$9KW:K 610 $aComputational Bayesian Statistics$9KW:K 610 $aDecision Sciences$9KW:K 610 $aStochastic processes$9KW:K 610 $aTheoretical bayesian statistics$9KW:K 620 $aCH$dCham$3VANL001889 702 1$aFrühwirth-Schnatter$bSylvia$3VANV087531 712 12$aBayesian Young Statisticians Meeting$d2.$f2014$eVienna$3VANV087532 712 $aSpringer $3VANV108073$4650 801 $aIT$bSOL$c20260911$gRICA 856 4 $uhttp://dx.doi.org/10.1007/978-3-319-16238-6$zE-book ? Accesso al full-text attraverso riconoscimento IP di Ateneo, proxy e/o Shibboleth 899 $aBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA$1IT-CE0120$2VAN08 912 $fN 912 $aVAN00113419 950 $aBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA$d08DLOAD e-book 0118 $e08eMF118 20180109 996 $aBayesian statistics from methods to models and applications$91522591 997 $aUNICAMPANIA