Note—An Improved Conditional Monte Carlo Technique for the Stochastic Shortest Path Problem
提出一种改进的条件蒙特卡洛模拟方法，利用均匀有向割集和唯一弧的性质，降低随机弧长网络中最短路径长度分布估计的采样成本，实验表明该方法比传统蒙特卡洛更高效。
This paper describes a simulation procedure for estimating the distribution function of the shortest path length in a network with random arc lengths. The method extends the concept of conditional Monte Carlo utilizing special properties of the Uniformly Directed Cutsets and the unique arcs. The objective here is to reduce the sampling effort and utilize known probability information to derive multivariate integrals of lower dimension. The experimental results show that the proposed method is substantially cost effective and performs better than traditional Monte Carlo and conditional methods.