Strictly proper kernel scores and characteristic kernels on compact spaces

نویسندگان

چکیده

Strictly proper kernel scores are well-known tools in probabilistic forecasting, while characteristic kernels have been extensively investigated machine learning. We show that both notions coincide, so insights from one part of the literature can be used other. metric induced by a cannot reliably distinguish between distributions far apart total variation norm as soon underlying space measures is infinite dimensional. describe terms eigenvalues and eigenfunctions apply this characterization to continuous on (locally) compact spaces. In case, we exist if only metrizable. As special cases investigate translation-invariant Abelian groups isotropic spheres. The latter interest for forecast evaluation predictions spherical domains encountered meteorology climatology.

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ژورنال

عنوان ژورنال: Applied and Computational Harmonic Analysis

سال: 2021

ISSN: ['1096-603X', '1063-5203']

DOI: https://doi.org/10.1016/j.acha.2019.11.005