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Numerical issues in maximum likelihood parameter estimation for Gaussian process interpolation

Abstract : This article investigates the origin of numerical issues in maximum likelihood parameter estimation for Gaussian process (GP) interpolation and investigates simple but effective strategies for improving commonly used open-source software implementations. This work targets a basic problem but a host of studies, particularly in the literature of Bayesian optimization, rely on off-the-shelf GP implementations. For the conclusions of these studies to be reliable and reproducible, robust GP implementations are critical.
Keywords : Gaussian process
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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 : Monday, August 2, 2021 - 9:34:32 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 interpolation. The 7th International Conference on machine Learning, Optimization and Data science - LOD 2021, Oct 2021, Grasmere, Lake District, England, United Kingdom. ⟨hal-03119528v1⟩

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