随机化学反应网络中样本路径与部分热力学极限的耦合
Coupling sample paths to the partial thermodynamic limit in stochastic chemical reaction networks
摘要：
许多生物化学系统具有多尺度结构，使得它们在热力学极限下收敛于分段确定的马尔可夫过程。分段确定过程的统计量可以比精确过程的统计量更有效地获得。我们探讨了将精确模型的样本路径耦合到分段确定性过程的可能性，以减小它们之间差异的方差。然后，我们应用这种耦合，以减少蒙特卡罗估计的计算复杂性。除了严格的结果有关的渐近计算复杂性的Monte Carlo估计，数值模拟进行了一些简单的生物模型，确认计算增益。
英文摘要：
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. In addition to rigorous results concerning the asymptotic computational complexity of the Monte Carlo estimator, numerical simulations are performed on some simple biological models confirming that computational gains are made.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
