隨機元件的網路系統績效通常是難以評估的。而隨機系統的模擬往往變成唯一可行的方法來評估此網路且此估計量的好壞就形成了我們主要關心的重點。

重點抽樣是一個能夠在不增加抽樣成本前提下降低估計量變異程度的變異數縮減技術。在本研究裡，我們利用了重點抽樣
的方法來改變高可靠度網路的抽樣程序使得系統失效的稀少事件更容易發生。另外，
藉由關鍵元件的狀態當成控制變數來更進一步改善重點抽樣估計量。

最後我們利用模擬實驗來評估重點抽樣估計量和合併估計量的成效。

Performace of network with random components is usually difficult to evaluate. Simulation of the stochastic system often becomes the only feasible method to assess the network and quality of the estimator becomes the major concern.

Importance sampling is a variance reduction technique which reduces the variation of the estimator
without increasing the sampling effort. In this study, importance sampling technique is used to alter the sampling scheme of a highly reliable network system to make the rare event of system failure accur more frequently. Then, the state of a key component is used as a control variate to further improve
the importance sampling estimator.

Simulation experiments are performed to evaluate the performace of the importance estimator and the combined estimator.

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