标题： 一种具有不平衡细化的新型分层抽样器在网络可靠性评估中的应用 显示英文标题

标题： A novel stratified sampler with unbalanced refinement for network reliability assessment

摘要： 我们研究了在网络可靠性评估中的分层抽样方法。 我们提出了一种不平衡分层细化程序，该程序基于网络组件划分为聚类以及每个聚类内的失效组件数量。 每个细化分层的大小及其相关的条件失效概率，统称为失效特征，可以使用条件伯努利模型计算和估计。 通过确定导致系统失效的最小组件失效数量$i^*$，并仅考虑至少有$i^*$个失效组件的分层，进一步改进了估计器。 我们提出了一个启发式但可行的关于所有分层最优样本量的近似方法，假设网络性能函数是一致的。 通过两个网络可靠性问题，展示了所提出的具有不平衡细化的分层抽样器（SSuR）的效率。 显示英文摘要

摘要： We investigate stratified sampling in the context of network reliability assessment. We propose an unbalanced stratum refinement procedure, which operates on a partition of network components into clusters and the number of failed components within each cluster. The size of each refined stratum and the associated conditional failure probability, collectively termed failure signatures, can be calculated and estimated using the conditional Bernoulli model. The estimator is further improved by determining the minimum number of component failure $i^*$ to reach system failure and then by considering only strata with at least $i^*$ failed components. We propose a heuristic but practicable approximation of the optimal sample size for all strata, assuming a coherent network performance function. The efficiency of the proposed stratified sampler with unbalanced refinement (SSuR) is demonstrated through two network reliability problems.