Variance reduction techniques for chemical reaction network simulation - Beentjes, C (2020)
In recent decades stochastic models have become an indispensable tool when analysing quantitative biological data, which are often subject to noise, both from intrinsic and extrinsic sources. Though such models generate richer, and perhaps more realistic, behaviour than their counterpart deterministic models, they are also inherently more difficult to study. Since exact solutions describing the dynamics of stochastic models are scarce, one is often forced to resort to simulation of these models using Monte Carlo methods, which are generally easy to implement. However, the output of such computational methods is itself subject to statistical error, and consequently it is often computationally demanding to get numerical predictions from stochastic models to a satisfactory degree of accuracy.