Strong convergence and speed up of nested stochastic simulation algorithm

Can Huang, Di Liu

    Research output: Research - peer-reviewArticle

    • 2 Citations

    Abstract

    In this paper,we revisit the Nested Stochastic Simulation Algorithm(NSSA) for stochastic chemical reacting networks by first proving its strong convergence. We then study a speed up of the algorithm by using the explicit Tau-Leaping method as the Inner solver to approximate invariant measures of fast processes, for which strong error estimates can also be obtained. Numerical experiments are presented to demonstrate the validity of our analysis.

    LanguageEnglish (US)
    Pages1207-1236
    Number of pages30
    JournalCommunications in Computational Physics
    Volume15
    Issue number4
    DOIs
    StatePublished - Apr 2014

    Profile

    simulation
    estimates

    Keywords

    • Biochemical reacting network
    • Stochastic simulation algorithm
    • Strong convergence

    ASJC Scopus subject areas

    • Physics and Astronomy (miscellaneous)

    Cite this

    Strong convergence and speed up of nested stochastic simulation algorithm. / Huang, Can; Liu, Di.

    In: Communications in Computational Physics, Vol. 15, No. 4, 04.2014, p. 1207-1236.

    Research output: Research - peer-reviewArticle

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