Non-parametric warping via local scale estimation for non-stationary Gaussian process modelling

Sébastien Marmin; Jean Baccou; Jacques Liandrat; David Ginsbourger

doi:10.1117/12.2272408

24 August 2017 Non-parametric warping via local scale estimation for non-stationary Gaussian process modelling

Sébastien Marmin, Jean Baccou, Jacques Liandrat, David Ginsbourger

Proceedings Volume 10394, Wavelets and Sparsity XVII; 1039421 (2017) https://doi.org/10.1117/12.2272408
Event: SPIE Optical Engineering + Applications, 2017, San Diego, California, United States

Abstract

We tackle the problem of reconstructing functions possessing highly heterogeneous behaviour across the input space from scattered evaluations. Our main approach combines non-stationary Gaussian process (GP) modelling with wavelet local analysis. A warped GP model is assumed, and a novel stationarization algorithm is proposed that relies on successive inverse warpings based on local scale estimation. The approach is applied to two mechanical case studies highlighting promising prediction performance compared to state-of-the-art methods.

Citation Download Citation

Sébastien Marmin, Jean Baccou, Jacques Liandrat, and David Ginsbourger "Non-parametric warping via local scale estimation for non-stationary Gaussian process modelling", Proc. SPIE 10394, Wavelets and Sparsity XVII, 1039421 (24 August 2017); https://doi.org/10.1117/12.2272408

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