由於基本的蒙特卡羅方法（Monte Carlo Method）存在明顯的缺點包括抽樣效率相對較弱、需要大量樣本，因此有了變異數縮減技術(Variance Reduction Technique)，來改善上述缺點，而抽樣方法會影響到抽出樣本是否精準，所以抽樣方法也算是變異數縮減技術的一環；變異數縮減法包含了對偶變量(Antithetic Random Variables)、控制變量技術(Control variates)、分層抽樣(Stratification)、重要性抽樣(Importance Sampling)、拉丁超立方抽樣(Latin Hypercube Sampling)以及隨機亂數法(Common random numbers )。
交叉熵(Cross-entropy)策略是一種通用方法應用於組結合多極值優化以及罕見事件模擬(rare event )。 在本文中，交叉熵策略被應用於在重要性抽樣中尋找最佳的重要性機率密度函數（IPDF），然後通過拉丁超立方抽樣（LHS）對獲得的IPDF進行抽樣。最後，使用對偶變量法（ARV）技術可以再進一步降低測試函數的變異數。本篇使用的例子進行模擬結果說明，基於本篇交叉熵策略的新型組合抽樣方法（CSMCES）能夠在一定的精確度下有效地減小樣本量，提高效率。

Due to the apparent drawbacks of Monte Carlo method including comparatively lower sampling efficiency, requiring a large number of samples. Variance Reduction Technique used to improve those drawbacks. The method of sampling will affect the precision, so sampling methods also a part of variance reduction technique. Variance Reduction Technique includes Antithetic random variables(ARV), Control variates technique(CV), Stratification, Importance sampling(IS), Latin hypercube sampling (LHS)and Common random numbers(CRN).
Cross-entropy(CE) strategy provides a general, simple and efficient method for solving such problems like the quadratic assignment problem and the rare event-simulation. Before we are sampling, CE strategy is use to choose an importance probability density function(IPDF). And then use LHS to sampling the IPDF we got. After that, used ARV technique can be further to reduce the variance of test function. The simulation results of stochastic edge networks examples show that this combined sampling method with CE strategy (CSMCES) can enhance efficiency under certain level of precision and effectively reduce sample size.

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