# Control Variates for Stochastic Simulation of Chemical Reaction Networks

## TL;DR

This paper introduces a variance reduction method using control variates for stochastic simulations of chemical reaction networks, significantly decreasing the number of runs needed for accurate estimations.

## Contribution

It presents a novel on-line algorithm for selecting control variates based on moment constraints, improving computational efficiency in stochastic chemical modeling.

## Key findings

- Reduced variance in estimators across case studies
- Fewer simulation runs required for accurate results
- Demonstrated efficiency of the control variate approach

## Abstract

Stochastic simulation is a widely used method for estimating quantities in models of chemical reaction networks where uncertainty plays a crucial role. However, reducing the statistical uncertainty of the corresponding estimators requires the generation of a large number of simulation runs, which is computationally expensive. To reduce the number of necessary runs, we propose a variance reduction technique based on control variates. We exploit constraints on the statistical moments of the stochastic process to reduce the estimators' variances. We develop an algorithm that selects appropriate control variates in an on-line fashion and demonstrate the efficiency of our approach on several case studies.