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Model selection based on validation criteria for Gaussian process regression: An application with highlights on the predictive variance by Charles Demay; Bertrand Iooss; Loic Le Gratiet; Amandine Marrel is a Decision Sciences article available to read on EtoBox.

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## Abstract The manufacturing quality of industrial pieces is linked with the intrinsic concentration field of certain chemical species. Thus, from punctual measurements sparsely distributed on the piece, a robust estimation of the maximum concentration is very valuable. To this aim, Gaussian Process regression models may be used, providing useful confidence interval at each interpolation location. However, the specification of the underlying covariance function from sparse data may be arduous, both in terms of function form and hyperparameters. This specification problem, also referred to as a model selection problem, may be addressed in several ways, from expert knowledge to numerical estimation. Regarding the covariance function form, the predictivity coefficient is commonly used as a primary criteria and focuses on the prediction capabilities of the model predictor. In this paper, other criteria related to the correctness of the model variance, covariance and more generally to the whole conditional distribution are added. This methodology is applied on the Kriging of chemical measurements spatially distributed on circular domains, where several classical covariance functions ar

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Author

Charles Demay; Bertrand Iooss; Loic Le Gratiet; Amandine Marrel

Publisher

Wiley

Published

2021

Language

EN

Field

Decision Sciences (Social Sciences)