Invariances of random fields paths, with applications in Gaussian Process Regression

Abstract : We study pathwise invariances of centred random fields that can be controlled through the covariance. A result involving composition operators is obtained in second-order settings, and we show that various path properties including additivity boil down to invariances of the covariance kernel. These results are extended to a broader class of operators in the Gaussian case, via the Loève isometry. Several covariance-driven pathwise invariances are illustrated, including fields with symmetric paths, centred paths, harmonic paths, or sparse paths. The proposed approach delivers a number of promising results and perspectives in Gaussian process regression.
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https://hal.archives-ouvertes.fr/hal-00850436
Contributeur : David Ginsbourger <>
Soumis le : mardi 6 août 2013 - 14:32:48
Dernière modification le : mardi 23 octobre 2018 - 14:36:09
Document(s) archivé(s) le : mercredi 5 avril 2017 - 19:39:42

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Invariant_fields_GPR.pdf
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  • HAL Id : hal-00850436, version 1
  • ARXIV : 1308.1359

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David Ginsbourger, Olivier Roustant, Nicolas Durrande. Invariances of random fields paths, with applications in Gaussian Process Regression. 2013. 〈hal-00850436〉

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