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Numerical issues in maximum likelihood parameter estimation for Gaussian process regression

Abstract : This article focuses on numerical issues in maximum likelihood parameter estimation for Gaussian process regression (GPR). This article investigates the origin of the numerical issues and provides simple but effective improvement strategies. This work targets a basic problem but a host of studies, particularly in the literature of Bayesian optimization, rely on off-the-shelf GPR implementations. For the conclusions of these studies to be reliable and reproducible, robust GPR implementations are critical.
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Preprints, Working Papers, ...
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https://hal-centralesupelec.archives-ouvertes.fr/hal-03119528
Contributor : Emmanuel Vazquez <>
Submitted on : Sunday, January 24, 2021 - 5:34:56 PM
Last modification on : Saturday, May 1, 2021 - 3:46:27 AM
Long-term archiving on: : Sunday, April 25, 2021 - 6:12:49 PM

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Distributed under a Creative Commons Attribution - NonCommercial - NoDerivatives 4.0 International License

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  • HAL Id : hal-03119528, version 1
  • ARXIV : 2101.09747

Citation

Subhasish Basak, Sébastien Petit, Julien Bect, Emmanuel Vazquez. Numerical issues in maximum likelihood parameter estimation for Gaussian process regression. 2021. ⟨hal-03119528⟩

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