INFERENCE ON THE REPRODUCING KERNEL HILBERT SPACES
Keywords:
Gaussian processes, spline interpolation, reproducing kernel, Bayesian inference.DOI:
https://doi.org/10.17654/2277141722002Abstract
Learning and interpolation are two extreme variants of the same problem, the object of which is to construct a function which is supposed to reasonably approximate an unknown function of which only a certain number of samples are known. These problems appear in various frameworks which go from the solution of equations to the partial derivatives, while passing by the modeling. In this paper, we present the Bayesian method of reconstructing or approximating functions of some proposed functions, based on the Reproducing Kernel Hilbert Space using Gaussian processes.
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