Strong Convergence and Speed up of Nested Stochastic Simulation Algorithm

Content Provider	Semantic Scholar
Author	Huang, Can Liu, Di
Copyright Year	2014
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. AMS subject classifications: 65C30, 60H35
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://users.math.msu.edu/users/richardl/publication/kmcs.pdf
Language	English
Access Restriction	Open
Subject Keyword	Approximation algorithm Classification Convergence (action) Estimated Experiment Gillespie algorithm Numerical analysis Numerical method Simulation Solver Stochastic Processes Tau-leaping VHDL-AMS
Content Type	Text
Resource Type	Article