This paper presents a method for fast estimation of probabilities of rare events in stochastic networks, with a particular emphasis on coherent reliability systems. The method is based on the concepts of likelihood-ratios (LR), change of probability measure and the bottleneck-cut in the network. Both polynomial and exponential-time Monte Carlo estimators are defined, and conditions under which the time complexity of the proposed LR estimators is bounded by a polynomial are discussed. The accuracy of the method depends only on the size (cardinality) of the bottleneck-cut, not on the topology and actual size of the network. Supporting numerical results are presented, with the cardinality of the bottleneck-cut /spl les/20.
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机译：本文提出了一种快速估计随机网络中稀有事件概率的方法，特别着重于相干可靠性系统。该方法基于似然比（LR），概率测度变化和网络瓶颈的概念。定义了多项式和指数时间的蒙特卡洛估计，并讨论了在此条件下拟议的LR估计的时间复杂度受多项式限制的条件。该方法的准确性仅取决于瓶颈的大小（基数），而不取决于网络的拓扑和实际大小。给出了支持的数值结果，以及瓶颈消除/ spl les / 20的基数。
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