Interpolation in reproducing kernel Hilbert spaces based on random subdivision schemes

In this paper we present a Bayesian framework for interpolating data in a reproducing kernel Hilbert space associated with a random subdivision scheme, where not only approximations of the values of a function at some missing points can be obtained, but also uncertainty estimates for such predicted...

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Veröffentlicht in:	Journal of computational and applied mathematics 2017-02, Vol.311, p.342-353
Hauptverfasser:	Cotronei, Mariantonia, Di Salvo, Rosa, Holschneider, Matthias, Puccio, Luigia
Format:	Artikel
Sprache:	eng
Schlagworte:	Bayesian inversion Computation Gaussian Hilbert space Interpolation Kernels Mathematical models Reproduction Simulation of Gaussian processes Subdivision schemes Subdivisions Uncertainty
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