multivariate normal distribution中文什么意思

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用"multivariate normal distribution"造句

多變量常態(tài)分布
多元正態(tài)分布

normal distribution: 【統(tǒng)計(jì)學(xué)】正態(tài)分布。
multivariate distribution: 多變量分布; 多維變量分析; 多元分布
multivariate t distribution: 多元
multivariate normal inference: 多變量常態(tài)推論
multivariate normal population: 多元正態(tài)總體

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Of course , it is often effective to apply conventional multivariate statistical process control ( mspc ) to the process whose process variables are subjected ( or approximatively subjected ) to multivariate normal distribution
本文的研究正是著眼于克服這兩大假設(shè)條件，使過程監(jiān)控技術(shù)能更好地適用于實(shí)際工業(yè)生產(chǎn)過程而進(jìn)行的。
Lots of research results are obtained in this field , though which are always based on two assumptions : one is that process variables are subjected to multivariate normal distribution ; the other is that samples are subjected to independent and identical distribution ( iid )
在此領(lǐng)域雖已獲得了大量成果，但研究基本上是在過程檢測(cè)數(shù)據(jù)服從多元正態(tài)分布和獨(dú)立同分布的兩個(gè)假設(shè)下進(jìn)行的。
Moreover , pca and bsa with their application in process monitoring are simple described 2 ) due to the fact that process information is n ' t always subjected to multivariate normal distribution , a process monitoring method based on pca with support vector classifier is provided , which improves the monitoring performance
此外，還簡(jiǎn)要地描述了主元分析方法和盲源信號(hào)分析方法及它們?cè)谶^程監(jiān)控中的應(yīng)用。 2 )由于過程信息并非均服從正態(tài)分布，提出了一種基于支持向量分類器主元分析方法的過程監(jiān)控方法，仿真表明提高了過程監(jiān)控的性能。
Two primary mathematical tools used in this dissertation are principal component analysis ( pca ) and blind signal analysis ( bsa ) , which are both data - driven methods . pca is not only used as feature extracting method ( where process variables are subjected to multivariate normal distribution ) , but also as a tool for dimension reduction ; bsa is used to extract independent features or process blind source signals from process information in information theory sense , which is more effective than pca in describing the process
主元分析方法不僅作為一種過程特征的提取方法(在過程信息服從多元正態(tài)分布的情況下) ，而且也作為一種過程數(shù)據(jù)降維的主要工具(在過程盲源信號(hào)提取的情況下) ；盲源信號(hào)分析是從信息論的角度，從過程信息中提取出盡可能獨(dú)立的過程特征信號(hào)或過程原始信源信號(hào)，它具有比主元分析更好的刻畫過程運(yùn)行特征的性能。
The results of process monitoring indicate that this method is more effective than the process monitoring method based on conventional blind source signal separation . 6 ) due to the complexity of process information , a process monitoring method which applies independent component analysis and principal component analysis to extract nonnormal distributed process features and normal distributed process features is presented , which avoids the assumption that process information is subjected to multivariate normal distribution
8 )鑒于在過程中，過程信息的平穩(wěn)性并不確定，提出了一種不考慮過程平穩(wěn)性能的過程監(jiān)控方法，仿真表明該方法比基于傳統(tǒng)ica的過程監(jiān)控方法具有更少的誤報(bào)率和漏報(bào)率，而比基于mspc的過程監(jiān)控方法具有更少的誤報(bào)率，從而說明該方法的有效性。