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Strong Convergence and Speed up of Nested Stochastic Simulation Algorithm

Published online by Cambridge University Press:  03 June 2015

Can Huang  and
Di Liu
Can Huang*
Affiliation:
Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA
Di Liu*
Affiliation:
Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA
*
Email:canhuang@math.msu.edu
Corresponding author.Email:richardl@math.msu.edu
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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.

Keywords

65C3060H35Stochastic simulation algorithmbiochemical reacting networkstrong convergence
Type
Research Article
Information
Communications in Computational Physics , Volume 15 , Issue 4 , April 2014 , pp. 1207 - 1236
Copyright
Copyright © Global Science Press Limited 2014

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