• Corpus ID: 53974086

On reproducing kernels, and analysis of measures

@article{Jorgensen2018OnRK,
  title={On reproducing kernels, and analysis of measures},
  author={Palle E. T. Jorgensen and Feng Tian},
  journal={arXiv: Functional Analysis},
  year={2018}
}

• P. Jorgensen, Feng Tian
• Published 11 July 2018
• Computer Science, Mathematics
• arXiv: Functional Analysis

Starting with the correspondence between positive definite kernels on the one hand and reproducing kernel Hilbert spaces (RKHSs) on the other, we turn to a detailed analysis of associated measures and Gaussian processes. Point of departure: Every positive definite kernel is also the covariance kernel of a Gaussian process.
Given a fixed sigma-finite measure $\mu$, we consider positive definite kernels defined on the subset of the sigma algebra having finite $\mu$ measure. We show that then the corresponding Hilbert factorizations consist of signed measures, finitely additive, but not automatically sigma-additive. We give a necessary and sufficient condition for when the measures in the RKHS, and the Hilbert factorizations, are sigma-additive. Our emphasis is the case when $\mu$ is assumed non-atomic. By contrast, when $\mu$ is known to be atomic, our setting is shown to generalize that of Shannon-interpolation. Our RKHS-approach further leads to new insight into the associated Gaussian processes, their Ito calculus and diffusion. Examples include fractional Brownian motion, and time-change processes.