We investigate the computational challenge of improving the accuracy of the stochastic simulation estimation by inducing negative correlation through the anticorrelated variance reduction technique. A direct application of the technique to the stochastic simulation algorithm (SSA), employing the inverse transformation, is not efficient for simulating large networks because its computational cost is similar to the sum of independent simulation runs. We propose in this study a new algorithm that employs the propensity bounds of reactions, introduced recently in their rejection-based SSA, to correlate and synchronise the trajectories during the simulation. The selection of reaction firings by our approach is exact due to the rejection-based mechanism. In addition, by applying the anticorrelated variance technique to select reaction firings, our approach can induce substantial correlation between realisations, hence reducing the variance of the estimator. The computational advantage of our rejection-based approach in comparison with the traditional inverse transformation is that it only needs to maintain a single data structure storing propensity bounds of reactions, which is updated infrequently, hence achieving better performance.

中文翻译：

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用于生化反应随机模拟的有效反相关方差减少。

我们研究了通过反相关方差减少技术诱导负相关来提高随机模拟估计精度的计算挑战。将该技术直接应用于采用逆变换的随机模拟算法 (SSA) 对于模拟大型网络效率不高，因为其计算成本类似于独立模拟运行的总和。我们在这项研究中提出了一种新算法，该算法利用最近在其基于拒绝的 SSA 中引入的反应倾向界限，在模拟过程中关联和同步轨迹。由于基于拒绝的机制，我们的方法对反应激发的选择是准确的。此外，通过应用反相关方差技术来选择反应触发，我们的方法可以在实现之间产生实质性的相关性，从而减少估计量的方差。与传统的逆变换相比，我们的基于拒绝的方法的计算优势在于，它只需要维护一个存储反应倾向边界的数据结构，该数据结构不经常更新，因此可以获得更好的性能。