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Volume 15, Issue 4
Strong Convergence and Speed up of Nested Stochastic Simulation Algorithm

Can Huang & Di Liu

Commun. Comput. Phys., 15 (2014), pp. 1207-1236.

Published online: 2014-04

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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.

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COPYRIGHT: © Global Science Press

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@Article{CiCP-15-1207, author = {Can Huang and Di Liu}, title = {Strong Convergence and Speed up of Nested Stochastic Simulation Algorithm}, journal = {Communications in Computational Physics}, year = {2014}, volume = {15}, number = {4}, pages = {1207--1236}, 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.

}, issn = {1991-7120}, doi = {https://doi.org/10.4208/cicp.290313.051213s}, url = {http://global-sci.org/intro/article_detail/cicp/7135.html} }
TY - JOUR T1 - Strong Convergence and Speed up of Nested Stochastic Simulation Algorithm AU - Can Huang & Di Liu JO - Communications in Computational Physics VL - 4 SP - 1207 EP - 1236 PY - 2014 DA - 2014/04 SN - 15 DO - http://doi.org/10.4208/cicp.290313.051213s UR - https://global-sci.org/intro/article_detail/cicp/7135.html KW - AB -

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.

Can Huang and Di Liu. (2014). Strong Convergence and Speed up of Nested Stochastic Simulation Algorithm. Communications in Computational Physics. 15 (4). 1207-1236. doi:10.4208/cicp.290313.051213s
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