Mixing times for two classes of stochastically modeled reaction networks

TL;DR

This paper investigates the rate at which certain stochastically modeled reaction networks converge to their stationary distributions, establishing exponential ergodicity and uniform convergence for two classes of networks.

Contribution

It characterizes mixing times for two classes of reaction networks using Foster-Lyapunov criteria, filling a gap in understanding convergence rates.

Findings

Established exponential ergodicity for two classes of reaction networks.

Proved uniform convergence over initial states for one class.

Extended understanding of convergence rates beyond non-negative integer state spaces.

Abstract

The past few decades have seen robust research on questions regarding the existence, form, and properties of stationary distributions of stochastically modeled reaction networks. When a stochastic model admits a stationary distribution an important practical question is: what is the rate of convergence of the distribution of the process to the stationary distribution? With the exception of \cite{XuHansenWiuf2022} pertaining to models whose state space is restricted to the non-negative integers, there has been a notable lack of results related to this rate of convergence in the reaction network literature. This paper begins the process of filling that hole in our understanding. In this paper, we characterize this rate of convergence, via the mixing times of the processes, for two classes of stochastically modeled reaction networks. Specifically, by applying a Foster-Lyapunov criteria we establish exponential ergodicity for two classes of reaction networks introduced in \cite{anderson2018some}. Moreover, we show that for one of the classes the convergence is uniform over the initial state.