ENBIS: European Network for Business and Industrial Statistics

Kriging and Invariances

23 September 2008, 15:40 – 16:00

Abstract

Submitted by

David Ginsbourger

Authors

David Ginsbourger, Xavier Bay, Yann Richet, Laurent Carraro

Affiliation

Ecole des Mines de Saint-Etienne and Institut de Radioprotection et de Surete Nucleaire

Abstract

Learning a deterministic function using a gaussian process (Kriging) relies on the selection of a covariance kernel. When some prior information is available concerning symmetries of the function to be approximated, it is clearly unreasonable not to use it in the choice of the kernel or covariance function. We propose a characterization of the kernels which associated gaussian processes have their paths invariant under the action of a finite group of transformations. We then give an example of such symmetrical processes, built on the basis of stationary gaussian processes, and having interesting regularity properties. The applicability of the latter methodology is finally demonstrated with the help of toy examples and of an industrial test-case.

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