Some central limit theorems for Markov paths and some properties of Gaussian random fields
Our primary aim is to ''build'' versions of generalised Gaussian processes from simple, elementary components in such a way that as many as possible of the esoteric properties of these elusive objects become intuitive. For generalised Gaussian processes, or fields, indexed by smooth functions or measures on \({\mathbb{R}}^ d\), our building blocks will be simple Markov processes whose state space is \({\mathbb{R}}^ d\). Roughly speaking, by summing functions of the local times of the Markov processes we shall, via a central limit theorem type of result, obtain the Gaussian field. This central limit result, together with related results indicating how additive functionals of the Markov processes generate additive functionals of the fields, yield considerable insight into properties of generalized Gaussian processes such as Markovianess, self-similarity, ''locality'' of functionals, etc. Although the paper is comprised primarily of new results, and despite the fact that the subject matter is somewhat esoteric, our aims are primarily didactic and expository - we want to try to initiate the uninitiated into some of the mysteries of generalised processes via an easily understood model.
scientific article; zbMATH DE number 3996764
scientific article