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Additive covariance kernels for high-dimensional Gaussian process modeling

Abstract : Gaussian process models -also called Kriging models- are often used as mathematical approximations of expensive experiments. However, the number of observation required for building an emulator becomes unrealistic when using classical covariance kernels when the dimension of input increases. In oder to get round the curse of dimensionality, a popular approach is to consider simplified models such as additive models. The ambition of the present work is to give an insight into covariance kernels that are well suited for building additive Kriging models and to describe some properties of the resulting models.
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Contributor : Nicolas Durrande Connect in order to contact the contributor
Submitted on : Friday, November 25, 2011 - 3:24:46 PM
Last modification on : Sunday, April 4, 2021 - 10:22:03 AM
Long-term archiving on: : Sunday, February 26, 2012 - 2:31:56 AM


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  • HAL Id : hal-00644934, version 1
  • ARXIV : 1111.6233


Nicolas Durrande, David Ginsbourger, Olivier Roustant. Additive covariance kernels for high-dimensional Gaussian process modeling. Annales de la Faculté de Sciences de Toulouse, 2012, Tome 21 (numéro 3), p. 481-499. ⟨hal-00644934⟩



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