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Catalogue Information
Field name	Details
Dewey Class	519.5
Title	Learning in the Absence of Training Data (EBook /) / by Dalia Chakrabarty.
Author	Chakrabarty, Dalia
Other name(s)	SpringerLink (Online service)
Edition statement	1st ed. 2023.
Publication	Cham : : Springer International Publishing : : Imprint: Springer, , 2023.
Physical Details	XVIII, 227 p. 29 illus., 16 illus. in color. : online resource.
ISBN	9783031310119
Summary Note	This 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.:
Contents note	1 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.
Mode of acces to digital resource	Digital reproduction.-
	Cham :
	Springer International Publishing,
	2023. -
	Mode of access: World Wide Web. System requirements: Internet Explorer 6.0 (or higher) or Firefox 2.0 (or higher). Available as searchable text in PDF format.
System details note	Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users).
Internet Site	https://doi.org/10.1007/978-3-031-31011-9
Links to Related Works	Subject References: Bayesian Inference . Data mining . Data Mining and Knowledge Discovery . Probabilities . Probability Theory . Statistical Theory and Methods . Statistics . Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences . Authors: author . Chakrabarty, Dalia . Corporate Authors: SpringerLink (Online service) . Classification: 519.5 .

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Learning in the Absence of Training Data

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