2021/10/18 by Michael Backenköhler, Backenköhler, Michael, Luca Bortolussi +3
Biochemistry, Genetics and Molecular Biology · Engineering · Materials Science · #Advanced Control Systems Optimization #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Electrical engineering #Gene Regulatory Network Analysis #Machine Learning in Materials Science #Methodology (stat.ME) #Molecular Networks (q-bio.MN) #Quantitative Methods (q-bio.QM) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2110.09143
openalex publication_date 2021/10/18 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
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.