Gaussian Process Regression under Location Uncertainty using Monte Carlo Approximation
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Date
2023
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Mark
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Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
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Abstract
Gaussian Process Regression (GPR) is a commonstatistical framework for spatial function estimation. While itsflexibility and availability of closed-form estimation solutionafter training are its advantages, it suffers on applicabilityconstraints in scenarios with uncertain training positions. Thispaper presents the derivation of the exact GPR operating onuncertain training positions along with approximation of theresulting terms using Monte Carlo (MC) sampling. This methodis then implemented in a simulation environment and shown toimprove the estimation quality over the standard GPR approachwith uncertain training positions.
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Proceedings II of the 29st Conference STUDENT EEICT 2023: Selected papers. s. 222-226. ISBN 978-80-214-6154-3
https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_2_v2.pdf
https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_2_v2.pdf
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en
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© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií