Articles | Volume 10, issue 2
https://doi.org/10.5194/ascmo-10-143-2024
https://doi.org/10.5194/ascmo-10-143-2024
29 Oct 2024
 | 29 Oct 2024

A robust approach to Gaussian process implementation

Juliette Mukangango, Amanda Muyskens, and Benjamin W. Priest

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Cited articles

Banerjee, S., Gelfand, A. E., Finley, A. O., and Sang, H.: Gaussian predictive process models for large spatial data sets, J. R. Stat. Soc. B, 70, 825–848, 2008. a
Buchanan, J. J., Schneider, M. D., Armstrong, R. E., Muyskens, A. L., Priest, B. W., and Dana, R. J.: Gaussian process classification for galaxy blend identification in lsst, Astrophys. J., 924, 94, https://doi.org/10.3847/1538-4357/ac35ca, 2022. a
Cressie, N. and Johannesson, G.: Fixed rank kriging for very large spatial data sets, J. R. Stat. Soc. B, 70, 209–226, 2008. a
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Short summary
In this study, we investigated the performance of Gaussian process regression (GP) models in handling outlier-affected spatial datasets. Our findings emphasized that models with the proposed methods provided accurate predictions and reliable uncertainty quantification, showcasing resilience against outliers. Overall, our study contributes to advancing the understanding of GP regression in spatial contexts and offers practical solutions to enhance its applicability in outlier-rich environments.