LEADER 03468nam 22005535 450 001 996418262503316 005 20200702145507.0 010 $a3-030-42553-3 024 7 $a10.1007/978-3-030-42553-1 035 $a(CKB)4100000011267089 035 $a(MiAaPQ)EBC6210912 035 $a(DE-He213)978-3-030-42553-1 035 $a(PPN)248394746 035 $a(EXLCZ)994100000011267089 100 $a20200527d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aCase Studies in Applied Bayesian Data Science$b[electronic resource] $eCIRM Jean-Morlet Chair, Fall 2018 /$fedited by Kerrie L. Mengersen, Pierre Pudlo, Christian P. Robert 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (vi, 417 pages) $cillustrations 225 1 $aLecture Notes in Mathematics,$x0075-8434 ;$v2259 311 $a3-030-42552-5 330 $aPresenting a range of substantive applied problems within Bayesian Statistics along with their Bayesian solutions, this book arises from a research program at CIRM in France in the second semester of 2018, which supported Kerrie Mengersen as a visiting Jean-Morlet Chair and Pierre Pudlo as the local Research Professor. The field of Bayesian statistics has exploded over the past thirty years and is now an established field of research in mathematical statistics and computer science, a key component of data science, and an underpinning methodology in many domains of science, business and social science. Moreover, while remaining naturally entwined, the three arms of Bayesian statistics, namely modelling, computation and inference, have grown into independent research fields.While the research arms of Bayesian statistics continue to grow in many directions, they are harnessed when attention turns to solving substantive applied problems. Each such problem set has its own challenges and hence draws from the suite of research a bespoke solution. The book will be useful for both theoretical and applied statisticians, as well as practitioners, to inspect these solutions in the context of the problems, in order to draw further understanding, awareness and inspiration. . 410 0$aLecture Notes in Mathematics,$x0075-8434 ;$v2259 606 $aStatistics  606 $aProbabilities 606 $aBayesian Inference$3https://scigraph.springernature.com/ontologies/product-market-codes/S18000 606 $aProbability Theory and Stochastic Processes$3https://scigraph.springernature.com/ontologies/product-market-codes/M27004 606 $aApplied Statistics$3https://scigraph.springernature.com/ontologies/product-market-codes/S17000 615 0$aStatistics . 615 0$aProbabilities. 615 14$aBayesian Inference. 615 24$aProbability Theory and Stochastic Processes. 615 24$aApplied Statistics. 676 $a519.542 702 $aMengersen$b Kerrie L$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aPudlo$b Pierre$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRobert$b Christian P$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996418262503316 996 $aCase Studies in Applied Bayesian Data Science$91768612 997 $aUNISA