# Finite Markov chains coupled to general Markov processes and an application to metastability I

## TL;DR

This paper develops a method to couple diffusion processes with Markov chains using eigenfunctions, providing explicit formulas and analyzing metastability, especially in the context of small noise perturbations of dynamical systems.

## Contribution

It introduces a novel coupling technique between diffusions and Markov chains based on eigenfunctions, with explicit formulas and analysis of metastability phenomena.

## Key findings

- Coupling diffusion processes to Markov chains using eigenfunctions.
- Explicit formulas for conditional probabilities in the coupling.
- Analysis of metastability in double-well potentials.

## Abstract

We consider a diffusion given by a small noise perturbation of a dynamical system driven by a potential function with a finite number of local minima. The classical results of Freidlin and Wentzell show that the time this diffusion spends in the domain of attraction of one of these local minima is approximately exponentially distributed and hence the diffusion should behave approximately like a Markov chain on the local minima. By the work of Bovier and collaborators, the local minima can be associated with the small eigenvalues of the diffusion generator. Applying a Markov mapping theorem, we use the eigenfunctions of the generator to couple this diffusion to a Markov chain whose generator has eigenvalues equal to the eigenvalues of the diffusion generator that are associated with the local minima and establish explicit formulas for conditional probabilities associated with this coupling. The fundamental question then becomes to relate the coupled Markov chain to the approximate Markov chain suggested by the results of Freidlin and Wentzel. In Part II of this work, we provide a complete analysis of this relationship in the special case of a double-well potential in one dimension. More generally, the coupling can be constructed for a general class of Markov processes and any finite set of eigenvalues of the generator.