Sparse Variational Contaminated Noise Gaussian Process Regression with Applications in Geomagnetic Perturbations Forecasting-Reference-Cited by-同舟云学术

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

Published:2024-08-05 Issue:1 Volume:3 Page:
ISSN:2694-1899
Container-title:Data Science in Science
language:en
Short-container-title:Data Science in Science

Author:

Iong Daniel¹^ORCID,McAnear Matthew¹,Qu Yuezhou¹,Zou Shasha²^ORCID,Toth Gabor²^ORCID,Chen Yang¹³^ORCID

Affiliation:

1. Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA

2. Department of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, Michigan, USA

3. Michigan Institute for Data Science, University of Michigan, Ann Arbor, Michigan, USA

Funder

NSF SWQU

NSF DMS

NASA

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/26941899.2024.2383281

Reference42 articles.

1. Algikar P Mili L. 2023. Robust Gaussian process regression with Huber likelihood. Number: arXiv:2301.07858 arXiv:2301.07858 [stat].

2. Altamirano M Briol F-X Knoblauch J. 2023. Robust and conjugate Gaussian process regression.

3. Variational inference: a review for statisticians;Blei DM;J Am Stat Assoc,2017

4. A Bayesian approach to some outlier problems;Box GEP;Biometrika,1968

5. A gray-box model for a probabilistic estimate of regional ground magnetic perturbations: enhancing the NOAA operational geospace model with machine learning;Camporeale E;J Geophys Res Space Phys,2020

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