Nested Kriging predictions for datasets with large number of observations

Abstract : This work falls within the context of predicting the value of a real function at some input locations given a limited number of observations of this function. The Kriging interpolation technique (or Gaussian process regression) is often considered to tackle such a problem but the method suffers from its computational burden when the number of observation points is large. We introduce in this article nested Kriging predictors which are constructed by aggregating sub-models based on subsets of observation points. This approach is proven to have better theoretical properties than other aggregation methods that can be found in the literature. Contrarily to some other methods it can be shown that the proposed aggregation method is consistent. Finally, the practical interest of the proposed method is illustrated on simulated datasets and on an industrial test case with $10^4$ observations in a 6-dimensional space.
Type de document :
Pré-publication, Document de travail
2016
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https://hal.archives-ouvertes.fr/hal-01345959
Contributeur : Nicolas Durrande <>
Soumis le : jeudi 20 juillet 2017 - 11:46:35
Dernière modification le : mercredi 29 novembre 2017 - 14:31:54

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Nested_Kriging_predictions.pdf
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  • HAL Id : hal-01345959, version 3
  • ARXIV : 1607.05432

Citation

Didier Rullière, Nicolas Durrande, François Bachoc, Clément Chevalier. Nested Kriging predictions for datasets with large number of observations. 2016. 〈hal-01345959v3〉

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