001471767 000__ 04359cam\\22005777i\4500
001471767 001__ 1471767
001471767 003__ OCoLC
001471767 005__ 20230908003313.0
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001471767 007__ cr\un\nnnunnun
001471767 008__ 230716s2023\\\\sz\\\\\\ob\\\\001\0\eng\d
001471767 020__ $$a303131011X$$qelectronic book
001471767 020__ $$a9783031310119$$q(electronic bk.)
001471767 020__ $$z3031310101
001471767 020__ $$z9783031310102
001471767 0247_ $$a10.1007/978-3-031-31011-9$$2doi
001471767 035__ $$aSP(OCoLC)1390553968
001471767 040__ $$aYDX$$beng$$erda$$cYDX$$dGW5XE$$dEBLCP$$dYDX$$dN$T$$dOCLCQ
001471767 049__ $$aISEA
001471767 050_4 $$aQ325.5$$b.C43 2023
001471767 08204 $$a006.31015195$$223/eng/20230721
001471767 1001_ $$aChakrabarty, Dalia,$$eauthor.
001471767 24510 $$aLearning in the absence of training data /$$cDalia Chakrabarty.
001471767 264_1 $$aCham :$$bSpringer,$$c2023.
001471767 300__ $$a1 online resource
001471767 336__ $$atext$$btxt$$2rdacontent
001471767 337__ $$acomputer$$bc$$2rdamedia
001471767 338__ $$aonline resource$$bcr$$2rdacarrier
001471767 504__ $$aIncludes bibliographical references and index.
001471767 5050_ $$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.
001471767 506__ $$aAccess limited to authorized users.
001471767 520__ $$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 systems 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 systems 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.
001471767 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed July 21, 2023).
001471767 650_0 $$aMachine learning$$xStatistical methods.
001471767 650_0 $$aBayesian statistical decision theory.
001471767 655_0 $$aElectronic books.
001471767 77608 $$iPrint version:$$z3031310101$$z9783031310102$$w(OCoLC)1373337772
001471767 852__ $$bebk
001471767 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-31011-9$$zOnline Access$$91397441.1
001471767 909CO $$ooai:library.usi.edu:1471767$$pGLOBAL_SET
001471767 980__ $$aBIB
001471767 980__ $$aEBOOK
001471767 982__ $$aEbook
001471767 983__ $$aOnline
001471767 994__ $$a92$$bISE