Unified Moment-Based Modeling of Integrated Stochastic Processes
提出一种基于矩的建模框架，用于精确模拟随机微分方程的解，克服了传统离散化方法的偏差和计算负担，适用于复杂随机模型的高效模拟。
Dial M for Simulation For years, systems of stochastic differential equations (SDEs) were simulated by discretization, inevitably introducing a bias, which can be difficult to quantify accurately. To circumvent this, some attempts have been made to simulate exactly various models from the SDE solution. These approaches prove capable of producing accurate results. A serious drawback of such an approach, nevertheless, is the implicit need to use extensive numerical methods, which make the entire simulation computationally heavy and quite impracticable. In the paper “Unified moment-based modeling of integrated stochastic processes,” Kyriakou, Brignone, and Fusai present a methodological framework based on M(oments) for the simulation of such models that overcomes earlier limitations. Theoretical results and extensive numerical experiments show that the proposed approach allows accurate simulation of complex stochastic models with low computational effort.