Large deviations of the empirical measures of a strong-Feller Markov process inside a subset and quasi-ergodic distribution

Abstract

In this work, we establish, for a strong Feller process, the large deviation principle for the occupation measure conditioned not to exit a given subregion. The rate function vanishes only at a unique measure, which is the so-called quasi-ergodic distribution of the process in this subregion. In addition, we show that the rate function is the Dirichlet form in the particular case when the process is reversible. We apply our results to several stochastic processes such as the solutions of elliptic stochastic differential equations driven by a rotationally invariant $\alpha$-stable process, the kinetic Langevin process, and the overdamped Langevin process driven by a Brownian motion.