Kernel-based Approximation Methods for Generalized Interpolations: A Deterministic or Stochastic Problem?
For researchers in approximation theory and meshfree methods, this work provides a novel stochastic perspective on deterministic interpolation problems.
The paper solves a deterministically generalized interpolation problem using a stochastic approach, introducing a kernel-based probability measure to construct and analyze estimators for non-noise or noisy data, and applies it to elliptic PDEs.
In this article, we solve a deterministically generalized interpolation problem by a stochastic approach. We introduce a kernel-based probability measure on a Banach space by a covariance kernel which is defined on the dual space of the Banach space. The kernel-based probability measure provides a numerical tool to construct and analyze the kernel-based estimators conditioned on non-noise data or noisy data including algorithms and error analysis. Same as meshfree methods, we can also obtain the kernel-based approximate solutions of elliptic partial differential equations by the kernel-based probability measure.