Sparse Variational Contaminated Noise Gaussian Process Regression with Applications in Geomagnetic Perturbations Forecasting

Sparse Variational Contaminated Noise Gaussian Process Regression with Applications in Geomagnetic Perturbations Forecasting

Gaussian Processes (GP) have become popular machine learning methods for kernel based learning on datasets with complicated covariance structures. In this paper, we present a novel extension to the GP framework using a contaminated normal likelihood function to better account for heteroscedastic var...

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Bibliographic Details
Main Authors: Daniel Iong, Matthew McAnear, Yuezhou Qu, Shasha Zou, Gabor Toth, Yang Chen
Format: Article
Language:English
Published: Taylor & Francis Group 2024-12-01
Series:Data Science in Science
Subjects:
Gaussian Process
regression
contaminated normal
SuperMAG
DeltaB
Online Access:https://www.tandfonline.com/doi/10.1080/26941899.2024.2383281
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