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arXiv · 1912.00401

Long-time asymptotics of stochastic reaction systems

Daniele Cappelletti·Abhishek Pal Majumder·Carsten Wiuf
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Abstract

We study the stochastic dynamics of a system of interacting species in a stochastic environment by means of a continuous-time Markov chain with transition rates depending on the state of the environment. Models of gene regulation in systems biology take this form. We characterise the finite-time distribution of the Markov chain, provide conditions for ergodicity, and characterise the stationary distribution (when it exists) as a mixture of Poisson distributions. The mixture measure is uniquely identified as the law of a fixed point of a stochastic recurrence equation. This recursion is crucial for statistical computation of moments and other distributional features.

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Daniele Cappelletti, Abhishek Pal Majumder, Carsten Wiuf. 2019-12-01. Long-time asymptotics of stochastic reaction systems. https://arxiv.org/abs/1912.00401

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