Continuous Disintegrations of Gaussian Processes

TL;DR

This paper introduces the concept of continuous disintegrations for Gaussian processes, establishing a necessary and sufficient condition based on covariance structure for their existence, with implications for understanding conditioned stochastic processes.

Contribution

It formalizes the notion of continuous disintegrations in infinite-dimensional settings and provides a key criterion based on a covariance-related quantity for Gaussian measures.

Findings

Finiteness of M is necessary and sufficient for continuous disintegration.

For stationary processes, M equals 1, satisfying the condition.

The framework applies to Gaussian measures in Banach spaces.

Abstract

The goal of this paper is to understand the conditional law of a stochastic process once it has been observed over an interval. To make this precise, we introduce the notion of a continuous disintegration: a regular conditional probability measure which varies continuously in the conditioned parameter. The conditioning is infinite-dimensional in character, which leads us to consider the general case of probability measures in Banach spaces. Our main result is that for a certain quantity $M$ based on the covariance structure, the finiteness of M is a necessary and sufficient condition for a Gaussian measure to have a continuous disintegration. The condition is quite reasonable: for the familiar case of stationary processes, M = 1.