LEADER 04894nam 22006975 450 001 9910299782403321 005 20250504235535.0 010 $a3-319-16238-1 024 7 $a10.1007/978-3-319-16238-6 035 $a(CKB)3710000000415679 035 $a(EBL)2095779 035 $a(SSID)ssj0001501353 035 $a(PQKBManifestationID)11918404 035 $a(PQKBTitleCode)TC0001501353 035 $a(PQKBWorkID)11524648 035 $a(PQKB)10694536 035 $a(DE-He213)978-3-319-16238-6 035 $a(MiAaPQ)EBC2095779 035 $a(PPN)186028229 035 $a(EXLCZ)993710000000415679 100 $a20150519d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBayesian Statistics from Methods to Models and Applications $eResearch from BAYSM 2014 /$fedited by Sylvia Frühwirth-Schnatter, Angela Bitto, Gregor Kastner, Alexandra Posekany 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (175 p.) 225 1 $aSpringer Proceedings in Mathematics & Statistics,$x2194-1017 ;$v126 300 $aDescription based upon print version of record. 311 08$a3-319-16237-3 320 $aIncludes bibliographical references and index. 327 $aOn Bayesian based adaptive confidence sets for linear functionals -- A new finite approximation for the NGG mixture model: an application to density estimation -- Distributed Estimation of Mixture Models -- Bayesian Survival Model based on Moment Characterization -- Identifying the Infectious Period Distribution for Stochastic Epidemic Models Using the Posterior Predictive Check -- A subordinated stochastic process model -- Jeffreys priors for mixture estimation -- Bayesian Variable Selection for Generalized Linear Models Using the Power-Conditional-Expected-Posterior Prior -- A new strategy for testing cosmology with simulations -- Mixture Model for Filtering Firms' Profit Rates -- Bayesian Estimation of the Aortic Stiffness based on Non-Invasive Computed Tomography Images -- Formal and Heuristic Model Averaging Methods for Predicting the US Unemployment Rate -- Bayesian Filtering for Thermal Conductivity Estimation given Temperature Observations -- Application of Interweaving in DLMs to an Exchange and Specialization Experiment. 330 $aThe Second Bayesian Young Statisticians Meeting (BAYSM 2014) and the research presented here facilitate connections among researchers using Bayesian Statistics by providing a forum for the development and exchange of ideas. WU Vienna University of Business and Economics hosted BAYSM 2014 from September 18th to 19th. The guidance of renowned plenary lecturers and senior discussants is a critical part of the meeting and this volume, which follows publication of contributions from BAYSM 2013. The meeting's scientific program reflected the variety of fields in which Bayesian methods are currently employed or could be introduced in the future. Three brilliant keynote lectures by Chris Holmes (University of Oxford), Christian Robert (Université Paris-Dauphine), and Mike West (Duke University), were complemented by 24 plenary talks covering the major topics Dynamic Models, Applications, Bayesian Nonparametrics, Biostatistics, Bayesian Methods in Economics, and Models and Methods, as well as a lively poster session with 30 contributions. Selected contributions have been drawn from the conference for this book. All contributions in this volume are peer-reviewed and share original research in Bayesian computation, application, and theory. 410 0$aSpringer Proceedings in Mathematics & Statistics,$x2194-1017 ;$v126 606 $aStatistics 606 $aStatistics 606 $aMathematical statistics$xData processing 606 $aStatistical Theory and Methods 606 $aStatistics in Business, Management, Economics, Finance, Insurance 606 $aStatistics and Computing 615 0$aStatistics. 615 0$aStatistics. 615 0$aMathematical statistics$xData processing. 615 14$aStatistical Theory and Methods. 615 24$aStatistics in Business, Management, Economics, Finance, Insurance. 615 24$aStatistics and Computing. 676 $a519.542 702 $aFrühwirth-Schnatter$b Sylvia$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBitto$b Angela$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKastner$b Gregor$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aPosekany$b Alexandra$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299782403321 996 $aBayesian statistics from methods to models and applications$91522591 997 $aUNINA