On degeneracy and invariances of random fields paths with applications in Gaussian process modelling

Abstract : We study pathwise invariances and degeneracies of random fields with motivating applications in Gaussian process modelling. The key idea is that a number of structural properties one may wish to impose a priori on functions boil down to degeneracy properties under well-chosen linear operators. We first show in a second order set-up that almost sure degeneracy of random field paths under some class of linear operators defined in terms of signed measures can be controlled through the two first moments. A special focus is then put on the Gaussian case, where these results are revisited and extended to further linear operators thanks to state-of-the-art representations. Several degeneracy properties are tackled, including random fields with symmetric paths, centred paths, harmonic paths, or sparse paths. The proposed approach delivers a number of promising results and perspectives in Gaussian process modelling. In a first numerical experiment, it is shown that dedicated kernels can be used to infer an axis of symmetry. Our second numerical experiment deals with conditional simulations of a solution to the heat equation, and it is found that adapted kernels notably enable improved predictions of non-linear functionals of the field such as its maximum.
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Submitted on : Friday, October 30, 2015 - 10:18:10 AM
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David Ginsbourger, Olivier Roustant, Nicolas Durrande. On degeneracy and invariances of random fields paths with applications in Gaussian process modelling. Journal of Statistical Planning and Inference, Elsevier, 2016, Volume 170, pp.Pages 117-128. ⟨10.1016/j.jspi.2015.10.002⟩. ⟨emse-01222506⟩



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