LEADER 04662nam 22007695 450 001 9910632477903321 005 20251229071110.0 010 $a3-031-16427-X 024 7 $a10.1007/978-3-031-16427-9 035 $a(MiAaPQ)EBC7147234 035 $a(Au-PeEL)EBL7147234 035 $a(CKB)25483514400041 035 $a(PPN)266351077 035 $a(BIP)86491153 035 $a(BIP)85342947 035 $a(DE-He213)978-3-031-16427-9 035 $a(EXLCZ)9925483514400041 100 $a20221126d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNew Frontiers in Bayesian Statistics $eBAYSM 2021, Online, September 1?3 /$fedited by Raffaele Argiento, Federico Camerlenghi, Sally Paganin 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (122 pages) 225 1 $aSpringer Proceedings in Mathematics & Statistics,$x2194-1017 ;$v405 300 $aIncludes index. 311 08$aPrint version: Argiento, Raffaele New Frontiers in Bayesian Statistics Cham : Springer International Publishing AG,c2023 9783031164262 327 $a1 Andrej Srakar, Approximate Bayesian algorithm for tensor robust principal component analysis -- 2 Yuanqi Chu, Xueping Hu, Keming Yu, Bayesian Quantile Regression for Big Data Analysis -- 3 Peter Strong, Alys McAlphine, Jim Smith, Towards A Bayesian Analysis of Migration Pathways using Chain Event Graphs of Agent Based Models -- 4 Giorgos Tzoumerkas, Dimitris Fouskakis, Power-Expected-Posterior Methodology with Baseline Shrinkage Priors -- 5 Mica Teo, Sara Wade, Bayesian nonparametric scalar-on-image regression via Potts-Gibbs random partition models -- 6 Alessandro Colombi, Block Structured Graph Priors in Gaussian Graphical Models -- 7 Jessica Pavani, Paula Moraga, A Bayesian joint spatio-temporal model for multiple mosquito-borne diseases -- 8 Ivan Gutierrez, Luis Gutierrez, Danilo Alvare, A Bayesian nonparametric test for cross-group differences relative to a control -- 9 Francesco Gaffi, Antonio Lijoi, Igor Pruenster, Specification of the base measure of nonparametric priors via random means -- 10 Matteo Pedone, Raffaele Argiento, Francesco Claudio Stingo, Bayesian Nonparametric Predictive Modeling for Personalized Treatment Selection -- 11 Gabriel Calvo, carmen armero, Virgilio Gómez-Rubio, Guido Mazzinari, Bayesian growth curve model for studying the intra-abdominal volume during pneumoperitoneum for laparoscopic surgery. 330 $aThis book presents a selection of peer-reviewed contributions to the fifth Bayesian Young Statisticians Meeting, BaYSM 2021, held virtually due to the COVID-19 pandemic on 1-3 September 2021. Despite all the challenges of an online conference, the meeting provided a valuable opportunity for early career researchers, including MSc students, PhD students, and postdocs to connect with the broader Bayesian community. The proceedings highlight many different topics in Bayesian statistics, presenting promising methodological approaches to address important challenges in a variety of applications. The book is intended for a broad audience of people interested in statistics, and provides a series of stimulating contributions on theoretical, methodological, and computational aspects of Bayesian statistics. 410 0$aSpringer Proceedings in Mathematics & Statistics,$x2194-1017 ;$v405 606 $aMathematical statistics 606 $aStochastic processes 606 $aStochastic models 606 $aStochastic analysis 606 $aMarkov processes 606 $aMathematical Statistics 606 $aStochastic Networks 606 $aStochastic Modelling 606 $aStochastic Analysis 606 $aMarkov Process 606 $aStochastic Processes 615 0$aMathematical statistics. 615 0$aStochastic processes. 615 0$aStochastic models. 615 0$aStochastic analysis. 615 0$aMarkov processes. 615 14$aMathematical Statistics. 615 24$aStochastic Networks. 615 24$aStochastic Modelling. 615 24$aStochastic Analysis. 615 24$aMarkov Process. 615 24$aStochastic Processes. 676 $a519.542 676 $a519.542 702 $aArgiento$b Raffaele 702 $aCamerlenghi$b Federico 702 $aPaganin$b Sally 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910632477903321 996 $aNew frontiers in Bayesian Statistics$93088801 997 $aUNINA