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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| Principais autores: | , , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Taylor & Francis Group
2024-12-01
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| coleção: | Data Science in Science |
| Assuntos: | |
| Acesso em linha: | https://www.tandfonline.com/doi/10.1080/26941899.2024.2383281 |
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