Large deviations for Markov jump processes with uniformly diminishing rates

Andrea Agazzi, Luisa Andreis, Robert I.A. Patterson and D.R. Michiel Renger

Stochastic Processes and their Applications, 2022, vol. 152, issue C, 533-559

Abstract: We prove a large-deviation principle (ldp) for the sample paths of jump Markov processes in the small noise limit when, possibly, all the jump rates vanish uniformly, but slowly enough, in a region of the state space. We further discuss the optimality of our assumptions on the decay of the jump rates. As a direct application of this work we relax the assumptions needed for the application of ldps to, e.g., Chemical Reaction Network dynamics, where vanishing reaction rates arise naturally particularly the context of mass action kinetics.

Date: 2022
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DOI: 10.1016/j.spa.2022.06.017

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