Additive Covariance Kernels for High-Dimensional Gaussian Process Modeling

Abstract

http://afst.cedram.org/afst-bin/fitem?id=AFST_2012_6_21_3_481_0National audienceGaussian 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