Advances in Statistical Climatology, Meteorology and Oceanography
Advances in Statistical Climatology, Meteorology and Oceanography
ASCMO
Articles | Volume 10, issue 2
Articles | Volume 10, issue 2
https://doi.org/10.5194/ascmo-10-143-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-10-143-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 10, issue 2
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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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.
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