Coupling sample paths to the thermodynamic limit in Monte Carlo estimators with applications to gene expression

JOURNAL OF COMPUTATIONAL PHYSICS

卷:346

Article

Levien, Ethan¹  Bressloff, Paul C.¹ 

[1] Univ Utah, Dept Math, Salt Lake City, UT 84112 USA

关键词: Chemical reaction networks;    Monte Carlo;    Variance reduction;    Piecewise deterministic Markov process;    Gillespie algorithm;   

DOI  :  10.1016/j.jcp.2017.05.050

来源: Elsevier

【 摘 要 】

Many biochemical systems appearing in applications have a multiscale structure so that they converge to piecewise deterministic Markov processes in a thermodynamic limit. The statistics of the piecewise deterministic process can be obtained much more efficiently than those of the exact process. We explore the possibility of coupling sample paths of the exact model to the piecewise deterministic process in order to reduce the variance of their difference. We then apply this coupling to reduce the computational complexity of a Monte Carlo estimator. Motivated by the rigorous results in [1], we show how this method can be applied to realistic biological models with nontrivial scalings. (C) 2017 Elsevier Inc. All rights reserved.

【 授权许可】

Free