Adaptive aggregation method for the chemical master equation

The chemical master equation, which is often considered as an accurate stochastic description of general chemical systems, usually imposes intensive computational requirements when used to characterize molecular biological systems. The major challenge comes from the curse of dimensionally, which has been tackled by a few research papers. The essential goal is to aggregate the system efficiently with limited approximation error. This paper presents an adaptive way to implement the aggregation process using information collected from Monte Carlo methods. Numerical results show the effectiveness of the proposed algorithm despite the lack of explicit estimation of approximation error.