Strong convergence and speed up of nested stochastic simulation algorithm

Can Huang, Di Liu

Research output: Contribution to journalArticle

  • 3 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: Contribution to journalArticle

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