【摘要】 传统的Monte Carlo方法仿真稀有事件需要较长的时间,而重要抽样技术可以有效的缩短仿真时间,提高仿真效率。因此,提出一种新的重要抽样实现方法,用来估计仿真模型中的稀有事件的概率。先选取经典的指数变换方法构造重要抽样分布类,再利用极小化重要抽样估计量方差的方法寻找最优重要抽样分布函数。仿真结果显示了该算法在估计稀有事件概率方面的有效性。更多还原

【Abstract】 It usually takes long time to simulate rare event using traditional Monte Carlo method,while importance sampling techniques can effectively reduce the simulation time and improve simulation efficiency. A new implementation for importance sampling method to estimate rare event probability in simulation models was proposed. The classical exponential change of measure was adopted to construct the family of importance sampling distributions,and the optimal importance sampling distributions was obtained by minimizing the variance of importance sampling estimator. Numerical experiments have been conducted and the results indicate that the method can effectively estimate the rare event probabilities.更多还原