18 citations · 37 across the 9 of their papers we have counts for
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stat.CO2024
Identifiability and Sensitivity Analysis of Kriging Weights for the Matern Kernel
Amanda Muyskens, Benjamin W. Priest, Imene R. Goumiri +1
Gaussian process (GP) models are effective non-linear models for numerous scientific applications. However, computation of their hyperparameters can be difficult when there is a la…
stat.CO2021★ 6 cited
MuyGPs: Scalable Gaussian Process Hyperparameter Estimation Using Local Cross-Validation
Amanda Muyskens, Benjamin Priest, Imène Goumiri +1
Gaussian processes (GPs) are non-linear probabilistic models popular in many applications. However, naïve GP realizations require quadratic memory to store the covariance matrix an…