Steady State Sensitivity Analysis of Continuous Time Markov Chains

Ting Wang, Petr Plechac

Published 2018-04-02, updated 2018-05-10Version 2

In this paper, we study Monte Carlo estimators based on the likelihood ratio approach for steady-state sensitivity. We first extend the result of Glynn and Olvera-Cravioto [arXiv:1707.02659] to the setting of continuous time Markov chains (CTMC) with a countable state space which include models such as stochastic reaction kinetics and kinetic Monte Carlo lattice system. Then we show that the variance of the centered LR estimators do not grow in time. This result suggests that the centered estimators should be favored when the mixing time of the CTMC is large. We demonstrate a practical implication of this analysis on a numerical benchmark of two examples for the biochemical reaction networks.