Important Sampling Scheme Based on Minimal Cuts for Stochastic Network Unreliability Estimation

博士 === 國立臺灣科技大學 === 資訊管理系 === 101 === Computer simulation is often used to estimate the system unavailability of a stochastic network. However, sampling efforts required for estimating unavailability of a highly reliable network become formidable. An importance sampling scheme based on a subset of t...

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Main Authors: Wei-Ling Shih, 時維寧
Other Authors: Wei-Ning Yang
Format: Others
Language:en_US
Published: 2013
Online Access:http://ndltd.ncl.edu.tw/handle/69031821584278262877
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spelling ndltd-TW-101NTUS53960062015-10-13T22:06:54Z http://ndltd.ncl.edu.tw/handle/69031821584278262877 Important Sampling Scheme Based on Minimal Cuts for Stochastic Network Unreliability Estimation 隨機網路不可靠度之評估─植基於最小割集之重點抽樣機制 Wei-Ling Shih 時維寧 博士 國立臺灣科技大學 資訊管理系 101 Computer simulation is often used to estimate the system unavailability of a stochastic network. However, sampling efforts required for estimating unavailability of a highly reliable network become formidable. An importance sampling scheme based on a subset of the minimal cuts of the network is proposed. The idea is to keep the importance distribution close to the original state distribution over the region of system failure and directly generate the failure states. The proposed estimator is shown to be unbiased and the variance of the estimator is derived. Empirical results show that for highly reliable networks the proposed estimator can achieve substantial variance reduction. Wei-Ning Yang 楊維寧 2013 學位論文 ; thesis 82 en_US
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description 博士 === 國立臺灣科技大學 === 資訊管理系 === 101 === Computer simulation is often used to estimate the system unavailability of a stochastic network. However, sampling efforts required for estimating unavailability of a highly reliable network become formidable. An importance sampling scheme based on a subset of the minimal cuts of the network is proposed. The idea is to keep the importance distribution close to the original state distribution over the region of system failure and directly generate the failure states. The proposed estimator is shown to be unbiased and the variance of the estimator is derived. Empirical results show that for highly reliable networks the proposed estimator can achieve substantial variance reduction.
author2 Wei-Ning Yang
author_facet Wei-Ning Yang
Wei-Ling Shih
時維寧
author Wei-Ling Shih
時維寧
spellingShingle Wei-Ling Shih
時維寧
Important Sampling Scheme Based on Minimal Cuts for Stochastic Network Unreliability Estimation
author_sort Wei-Ling Shih
title Important Sampling Scheme Based on Minimal Cuts for Stochastic Network Unreliability Estimation
title_short Important Sampling Scheme Based on Minimal Cuts for Stochastic Network Unreliability Estimation
title_full Important Sampling Scheme Based on Minimal Cuts for Stochastic Network Unreliability Estimation
title_fullStr Important Sampling Scheme Based on Minimal Cuts for Stochastic Network Unreliability Estimation
title_full_unstemmed Important Sampling Scheme Based on Minimal Cuts for Stochastic Network Unreliability Estimation
title_sort important sampling scheme based on minimal cuts for stochastic network unreliability estimation
publishDate 2013
url http://ndltd.ncl.edu.tw/handle/69031821584278262877
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