Variance Reduction in Stochastic Reaction Networks using Control Variates
Methodology
2021-10-19 v1 Systems and Control
Systems and Control
Molecular Networks
Quantitative Methods
Abstract
Monte Carlo estimation in plays a crucial role in stochastic reaction networks. However, reducing the statistical uncertainty of the corresponding estimators requires sampling a large number of trajectories. We propose control variates based on the statistical moments of the process to reduce the estimators' variances. We develop an algorithm that selects an efficient subset of infinitely many control variates. To this end, the algorithm uses resampling and a redundancy-aware greedy selection. We demonstrate the efficiency of our approach in several case studies.
Cite
@article{arxiv.2110.09143,
title = {Variance Reduction in Stochastic Reaction Networks using Control Variates},
author = {Michael Backenköhler and Luca Bortolussi and Verena Wolf},
journal= {arXiv preprint arXiv:2110.09143},
year = {2021}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1905.00854