Department of Mathematics, University of Wisconsin, Madison, USA.
Bull Math Biol. 2019 Aug;81(8):2902-2930. doi: 10.1007/s11538-018-0430-6. Epub 2018 Apr 18.
A number of coupling strategies are presented for stochastically modeled biochemical processes with time-dependent parameters. In particular, the stacked coupling is introduced and is shown via a number of examples to provide an exceptionally low variance between the generated paths. This coupling will be useful in the numerical computation of parametric sensitivities and the fast estimation of expectations via multilevel Monte Carlo methods. We provide the requisite estimators in both cases.
针对时变参数的随机化生物化学过程，本文提出了多种耦合策略。具体而言，引入了堆叠式耦合，并通过多个实例展示了其能够显著降低生成路径之间的方差。这种耦合在参数灵敏度的数值计算和基于多层蒙特卡罗方法的快速期望估计中非常有用。我们在这两种情况下都提供了必要的估计器。
