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- W4311388919 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." @default.
- W4311388919 created "2022-12-25" @default.
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- W4311388919 date "2011-11-27" @default.
- W4311388919 modified "2023-09-27" @default.
- W4311388919 title "Additive Covariance Kernels for High-Dimensional Gaussian Process Modeling" @default.
- W4311388919 doi "https://doi.org/10.48550/arxiv.1111.6233" @default.
- W4311388919 hasPublicationYear "2011" @default.
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