Please use this identifier to cite or link to this item: doi:10.22028/D291-31064
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Title: Control Variates for Stochastic Simulation of Chemical Reaction Networks
Author(s): Backenköhler, Michael
Bortolussi, Luca
Wolf, Verena
Editor(s): Bortolussi, Luca
Sanguinetti, Guido
Language: English
Title: Computational methods in systems biology : 17th international conference
Startpage: 42
Endpage: 59
Publisher/Platform: Springer
Year of Publication: 2019
Place of publication: Cham
Title of the Conference: CMSB 2019
Place of the conference: Trieste, Italy
Publikation type: Conference Paper
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.
DOI of the first publication: 10.1007/978-3-030-31304-3_3
URL of the first publication: https://link.springer.com/chapter/10.1007/978-3-030-31304-3_3
Link to this record: hdl:20.500.11880/29205
http://dx.doi.org/10.22028/D291-31064
ISBN: 978-3-030-31303-6
978-3-030-31304-3
Date of registration: 29-May-2020
Notes: Lecture notes in computer science ; volume 11773
Faculty: MI - Fakultät für Mathematik und Informatik
Department: MI - Informatik
Professorship: MI - Prof. Dr. Verena Wolf
Collections:UniBib – Die Universitätsbibliographie

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