Harmonic analysis of network systems via kernels and their boundary realizations

With view to applications to harmonic and stochastic analysis of infinite network/graph models, we introduce new tools for realizations and transforms of positive definite kernels (p.d.) \begin{document}$ K $\end{document} and their associated reproducing kernel Hilbert spaces. With this we establish two kinds of factorizations: (i) Probabilistic: Starting with a positive definite kernel \begin{document}$ K $\end{document} we analyze associated Gaussian processes \begin{document}$ V $\end{document} . Properties of the Gaussian processes will be derived from certain factorizations of \begin{document}$ K $\end{document} , arising as a covariance kernel of \begin{document}$ V $\end{document} . (ii) Geometric analysis: We discuss families of measure spaces arising as boundaries for \begin{document}$ K $\end{document} . Our results entail an analysis of a partial order on families of p.d. kernels, a duality for operators and frames, optimization, Karhunen–Loève expansions, and factorizations. Applications include a new boundary analysis for the Drury-Arveson kernel, and for certain fractals arising as iterated function systems; and an identification of optimal feature spaces in machine learning models.