Large deviations for metastable states of Markov processes with absorbing states with applications to population models in stable or randomly switching environment

TL;DR

This paper develops a large deviation framework at Level 2.5 for Markov processes with absorbing states, enabling explicit calculation of extinction rates and analysis of metastable states in population models and diffusions.

Contribution

It introduces a comprehensive formalism for large deviations at Level 2.5, linking extinction rates to empirical observables and applying it to population and diffusion models.

Findings

Explicit extinction rates derived from empirical observables.

Spectral problem recovered from large deviation optimization.

Applicable to population models in stable or switching environments.

Abstract

The large deviations at Level 2.5 are applied to Markov processes with absorbing states in order to obtain the explicit extinction rate of metastable quasi-stationary states in terms of their empirical time-averaged density and of their time-averaged empirical flows over a large time-window $T$. The standard spectral problem for the slowest relaxation mode can be recovered from the full optimization of the extinction rate over all these empirical observables and the equivalence can be understood via the Doob generator of the process conditioned to survive up to time $T$. The large deviation properties of any time-additive observable of the Markov trajectory before extinction can be derived from the Level 2.5 via the decomposition of the time-additive observable in terms of the empirical density and the empirical flows. This general formalism is described for continuous-time Markov chains, with applications to population birth-death model in a stable or in a switching environment, and for diffusion processes in dimension $d$.