Fast variance reduction for steady-state simulation and sensitivity analysis of stochastic chemical systems using shadow function estimators

Andreas Milias-Argeitis, John Lygeros, Mustafa Khammash

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Abstract

We address the problem of estimating steady-state quantities associated to systems of stochastic chemical kinetics. In most cases of interest, these systems are analytically intractable, and one has to resort to computational methods to estimate stationary values of cost functions. In this work, we introduce a novel variance reduction algorithm for stochastic chemical kinetics, inspired by related methods in queueing theory, in particular the use of shadow functions. Using two numerical examples, we demonstrate the efficiency of the method for the calculation of steady-state parametric sensitivities and evaluate its performance in comparison to other estimation methods.
Original languageEnglish
Article number024104
Number of pages9
JournalJournal of Chemical Physics
Volume141
Issue number2
DOIs
Publication statusPublished - 14-Jul-2014
Externally publishedYes

Keywords

  • TIME MARKOV-CHAINS
  • POISSON EQUATION

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