importance sampling

The paper uses improved importance sampling and new convergent criterion to increase the speed of constringency.

算法设计上,采用改进的蒙特卡罗重要抽样法,并选取了全新的收敛判据,极大地提高了收敛速度.

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Importance sampling has wide application as an efficient reliability analysis approach.

重要抽样法是一种计算效率高而应用广泛的可靠性分析方法.

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Due to the shortcomings of general reliability analysis method of underground construction structures, a new method that combines artificial neural network and importance sampling Monte Carlo method is put forward for analyzing the reliability and applied to the rock-socketed support structure of supper-sized foundation pit.

针对一般地下施工结构可靠度理论方法的不足，提出人工神经网络结合重要性抽样的Monte Carlo可靠度分析方法，并应用于一特大超深基坑的嵌岩支护结构。

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In this paper, we apply Importance Sampling ( IS) technique to estimate the BERs of digital fiber optic communication links.

本文使用重要性抽样（IS）方法，对数字光纤通信链路的误码率参量进行了估计。

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It is efficient to apply importance sampling concepts to the sensitivity analysis.

利用重点取样法来做敏感度分析时,不需再重新模拟样本,可减少运算上的时间.

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In order to improve computational efficiency, this paper introduces RBF Neural Network to substitute structural performance function evaluations in Importance Sampling Monte-Carlo simulations and proposes RBF-Monte-Carlo approach for reducing structural analyses.

鉴于此，在蒙特卡罗重要抽样方法的基础上，提出了利用RBF神经网络替代原结构功能函数的RBF-蒙特卡罗方法，以提高工作效率。

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The undetected error probability of linear block codes for both error detection and correction is estimated with importance sampling.

研究用重要性抽样仿真方法估计线性分组码同时纠错和检错时的不可检错误概率。

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Stratified sampling and importance sampling are two Monte-carlo statistic methods. Software Markov-chain usage model is a mathematical model and preciously describes the usage of software.

分层抽样和重要抽样方法是两种常用的蒙特卡罗统计方法，Markov链使用模型是利用数学模型精确地描述软件的使用。

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Estimation of Undetected Error Probability of Block Codes Using Importance Sampling

用重要性抽样方法估计分组码的未检概率

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