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Numerical issues in maximum likelihood parameter estimation for Gaussian process interpolation

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 نشر من قبل Emmanuel Vazquez
 تاريخ النشر 2021
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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.

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