| 分段抽样模型中抽中目标的概率分析 |
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| 引用本文： | 杨观赐,李少波,钟勇.分段抽样模型中抽中目标的概率分析[J].计算机应用,2012,32(8):2209-2211. |
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| 作者姓名： | 杨观赐 李少波 钟勇 |
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| 作者单位： | 1. 教育部现代制造技术重点实验室(贵州大学)，贵阳 5500032. 中国科学院 成都计算机应用研究所, 成都6100413. 中国科学院 成都计算机应用研究所, 成都 610041 |
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| 基金项目： | 教育部新世纪优秀人才支持计划项目，国家科技支撑计划项目，贵州省科学技术基金资助项目，贵阳市科技局科技计划项目，贵州大学人才引进基金资助项目 |
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| 摘 要： | 为了增大基于种群操作的搜索技术在有限时间内捕捉到决策空间中的特定目标的概率，基于古典概率模型建立不划分的随机抽样模型和划分成多个子区域的随机抽样模型(简称划分模型)，分析比较了两个模型分别进行多次独立随机抽样至少抽中1次特定目标的概率，并证明：当总体中特定目标的数量为1或2时，划分模型抽中特定目标的概率恒大于不划分模型的概率。
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| 关 键 词： | 决策空间划分模型 古典概率模型 随机抽样 分段抽样 均匀分布 |
| 收稿时间： | 2012-02-15 |
| 修稿时间： | 2012-03-20 |
Probability analysis of capturing specific objects in stratified sampling model |
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| YANG Guan-ci
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LI Shao-bo
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ZHONG Yong.Probability analysis of capturing specific objects in stratified sampling model[J].journal of Computer Applications,2012,32(8):2209-2211. |
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| Authors: | YANG Guan-ci LI Shao-bo ZHONG Yong |
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| Affiliation: | 1. Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu Sichuan 610041, China2. Key Laboratory of Advanced Manufacturing Technology (Guizhou University), Ministry of Education, Guiyang Guizhou 550003, China3. Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu Sichuan 610041, China |
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| Abstract: | In order to increase the probability of population-based search approach to capture specific objects from the decision space within limited time,based on the classical probability model,this paper established an overall random sampling model and a partition sampling model inspired by stratified sampling which divided the sample space into more than one subspace.By analyzing and comparing the two random event’s probability of obtaining the specific objectives at least one time among repeatedly independent random sampling from those models,the paper proves that the partition sampling model’s probability is greater than the overall random sampling model’s probability permanently when the amount of specific objective in the collectivity is 1 or 2. |
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| Keywords: | decision space partition model classical probability model random sampling stratified sampling uniform distribution |
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