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covariance相关的网络例句

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与 covariance 相关的网络例句 [注:此内容来源于网络,仅供参考]

The concise and unified formula for testing any local parameter using F-statistic in covariance analysis is presented.The specific algorithm is also described.

给出了在协方差分析中,对任何局部参数进行检验时使用F-统计量的简明统一表达式,也给出了具体的算法。

In this paper, a new model adaptation using vector Taylor series is presented, which adapts the mean vector and covariance matrix of hidden Markov model.

该文将矢量泰勒级数用于模型自适应,同时对HMM模型的均值向量和协方差矩阵进行变换,使其与实际环境相匹配。

Moreover, it processes only a single snapshot data with no use for the formation of the sample covariance matrix and matrix inversion operation.

同时,它只对单快拍数据进行处理,避免了样本协方差矩阵的构造以及矩阵求逆运算,更适合于实时处理。

Analyzing the inner configuration of the covariance matrix, a new multiple targets DOA tracking algorithm is presented based on a matrix norm minimization technique.

摘要该文分析了协方差矩阵的内部结构,得到了一种具有较低运算量的基于范数最小准则的多目标DOA跟踪算法。

When oversampling factor is 2, the covariance matrix composed of the coefficients of correlation between those samples can be calculated by numerical method.

按Nyquist采样率均匀抽取出的采样点之间相互独立同分布,推广到过采样系数为2时,得到此时由各采样点之间相关系数构成的协方差矩阵,利用数值计算对其进行分析。

Getting out the Eigen values and eigenvectors of the series covariance matrix by the Jacobi algorithm in C language also is discussed in this paper.

本文使用C语言,利用雅可比算法,编写出可快速求出数据因子分析中所要求的矩阵特征值和特征向量的程序。

MWF holds a new filter structure in which only the cross correlation vectors of each stage instead of the covariance matrix of input series need to be estimated when filtering.

MWF是一种新型的滤波器结构,在滤波时,并不需要知道输入序列的协方差矩阵,只要估计各级的互相关矢量即可。

The commons among those methods is that all of them calculate the mean vector and the covariance matrix of sample then calculate other variables by using them.

这些方法有个共同的地方就是都会计算样本的均值向量和协方差矩阵,并在这两者的基础上计算其他统计量。

This paper focuses on the study of the most popular FAST-MCD method which is improved to overcome its shortcoming, constructs robust mean vector and robust covariance matrix which is applied in PCA method.

本文对目前较流行的FAST-MCD方法的算法进行研究,构造了稳健的均值向量和稳健的协方差矩阵,应用到主成分分析中,并针对其不足之处提出改进方法。

This dissertation focus on the study of the most popular FAST-MCD method which is improved by concentrating on its shortcoming, constructs robust mean vector and robust covariance matrix which is applied in Principal component analysis method and Factor analysis.

本文还著重地对现时最流行的FAST-MCD方法的算法进行研究,构造了稳健的均值向量和稳健的协方差矩阵,应用到主成分分析和因子分析中,并针对其不足之处提出改进方法。

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