  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.
