LEADER 04579nam 22006975 450 001 9910734827103321 005 20240522135439.0 010 $a3-031-31011-X 024 7 $a10.1007/978-3-031-31011-9 035 $a(MiAaPQ)EBC30645950 035 $a(Au-PeEL)EBL30645950 035 $a(DE-He213)978-3-031-31011-9 035 $a(PPN)272256218 035 $a(CKB)27578223000041 035 $a(EXLCZ)9927578223000041 100 $a20230713d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aLearning in the Absence of Training Data$b[electronic resource] /$fby Dalia Chakrabarty 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (241 pages) 311 08$aPrint version: Chakrabarty, Dalia Learning in the Absence of Training Data Cham : Springer International Publishing AG,c2023 9783031310102 320 $aIncludes bibliographical references and index. 327 $a1 Bespoke Learning to generate originally-absent training data -- 2 Forecasting by Learning Evolution-Driver - Application to Forecasting New COVID19 Infections -- 3 Potential to Density - Application to Learning Galactic Gravitational Mass Density -- 4 Bespoke Learning in Static Systems - Application to Learning Sub-surface Material Density Function -- 5 Bespoke Learning of Output using Inter-Network Distance - Application to Haematology-Oncology -- A Bayesian inference by posterior sampling using MCMC. 330 $aThis book introduces the concept of ?bespoke learning?, a new mechanistic approach that makes it possible to generate values of an output variable at each designated value of an associated input variable. Here the output variable generally provides information about the system?s behaviour/structure, and the aim is to learn the input-output relationship, even though little to no information on the output is available, as in multiple real-world problems. Once the output values have been bespoke-learnt, the originally-absent training set of input-output pairs becomes available, so that (supervised) learning of the sought inter-variable relation is then possible. Three ways of undertaking such bespoke learning are offered: by tapping into system dynamics in generic dynamical systems, to learn the function that causes the system?s evolution; by comparing realisations of a random graph variable, given multivariate time series datasets of disparate temporal coverage; and by designing maximally information-availing likelihoods in static systems. These methodologies are applied to four different real-world problems: forecasting daily COVID-19 infection numbers; learning the gravitational mass density in a real galaxy; learning a sub-surface material density function; and predicting the risk of onset of a disease following bone marrow transplants. Primarily aimed at graduate and postgraduate students studying a field which includes facets of statistical learning, the book will also benefit experts working in a wide range of applications. The prerequisites are undergraduate level probability and stochastic processes, and preliminary ideas on Bayesian statistics. 606 $aStatistics 606 $aData mining 606 $aProbabilities 606 $aStatistical Theory and Methods 606 $aBayesian Inference 606 $aData Mining and Knowledge Discovery 606 $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences 606 $aProbability Theory 606 $aAprenentatge automàtic$2thub 606 $aMètodes estadístics$2thub 606 $aEstadística bayesiana$2thub 608 $aLlibres electrònics$2thub 615 0$aStatistics. 615 0$aData mining. 615 0$aProbabilities. 615 14$aStatistical Theory and Methods. 615 24$aBayesian Inference. 615 24$aData Mining and Knowledge Discovery. 615 24$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 615 24$aProbability Theory. 615 7$aAprenentatge automàtic 615 7$aMètodes estadístics 615 7$aEstadística bayesiana 676 $a006.31015195 700 $aChakrabarty$b Dalia$01373522 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910734827103321 996 $aLearning in the Absence of Training Data$93404585 997 $aUNINA