Original title: Gaussian Process Regression under Location Uncertainty using Monte Carlo Approximation
Authors: Ptáček, Martin
Document type: Papers
Language: eng
Publisher: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
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.
Keywords: GPR; Monte Carlo approximation; Probabilistic inference; Spatial function estimation; Uncertaintraining positions
Host item entry: Proceedings II of the 29st Conference STUDENT EEICT 2023: Selected papers, ISBN 978-80-214-6154-3, ISSN 2788-1334

Institution: Brno University of Technology (web)
Document availability information: Fulltext is available in the Brno University of Technology Digital Library.
Original record: http://hdl.handle.net/11012/210695

Permalink: http://www.nusl.cz/ntk/nusl-531801


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