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

Viewed

Total article views: 147 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
132 13 2 147 2 2
  • HTML: 132
  • PDF: 13
  • XML: 2
  • Total: 147
  • BibTeX: 2
  • EndNote: 2
Views and downloads (calculated since 29 Oct 2024)
Cumulative views and downloads (calculated since 29 Oct 2024)

Viewed (geographical distribution)

Total article views: 134 (including HTML, PDF, and XML) Thereof 134 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 20 Nov 2024
Download
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.