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OverviewFull Product DetailsAuthor: Dalia ChakrabartyPublisher: Springer International Publishing AG Imprint: Springer International Publishing AG Edition: 1st ed. 2023 Weight: 0.541kg ISBN: 9783031310102ISBN 10: 3031310101 Pages: 227 Publication Date: 14 July 2023 Audience: College/higher education , Postgraduate, Research & Scholarly Format: Hardback Publisher's Status: Active Availability: Manufactured on demand  We will order this item for you from a manufactured on demand supplier. Table of Contents1 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.ReviewsAuthor InformationDr. Dalia Chakrabarty has a D.Phil in Astrophysics from the University of Oxford, which she pursued after obtaining an M.S. from the Department of Physics at the Indian Institute of Science. Following her doctoral work, she diversified into developing methodologies for the learning of properties in generic systems, given variously challenging data situations, and making applications of such methods to various real-world problems across disciplines. She works in the Department of Mathematics, at Brunel University London, and her main areas of interest include mathematical foundations of Machine Learning (ML) within a Bayesian paradigm. Tab Content 6Author Website:Countries AvailableAll regions |
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