Kusuoka-Stroock gradient bounds for the solution of the filtering equation

TL;DR

This paper extends classical gradient bounds to stochastic PDE solutions with random perturbations, providing pathwise estimates crucial for nonlinear filtering applications.

Contribution

It introduces pathwise gradient bounds for stochastic PDEs with random perturbations, extending Kusuoka-Stroock's heat semigroup analysis to filtering problems.

Findings

Derived sharp pathwise gradient bounds for stochastic PDE solutions.

Extended classical heat semigroup analysis to stochastic filtering context.

Provided asymptotic estimates with applications to nonlinear filtering.

Abstract

We obtain sharp gradient bounds for perturbed diffusion semigroups. In contrast with existing results, the perturbation is here random and the bounds obtained are pathwise. Our approach builds on the classical work of Kusuoka and Stroock [7],[9],[10],[11], and extends their program developed for the heat semi-group to solutions of stochastic partial differential equations. The work is motivated by and applied to nonlinear filtering. The analysis allows us to derive pathwise gradient bounds for the un-normalised conditional distribution of a partially observed signal. It uses a pathwise representation of the perturbed semigroup in the spirit of classical work by Ocone [14]. The estimates we derive have sharp small time asymptotics.