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sample covariance matrix相关的网络例句

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

The paper consists of three parts. First, we give the calculation for every order central moment for the Logistic population, mean vector and covariance matrix of the order statistic of random sample from the Logistic population, and means of several special functions for the order statistic of random sample from the Logistic population. Secondly, we discuss the parameter estimation for the Logistic population. Finally, we solve the question about the goodness-of -fit test for the Logistic population.

研究的主要内容分三部分:第一部分是关于Logistic总体各阶中心矩、样本次序统计量期望向量与协方差阵及若干个特殊的样本次序统计量函数期望的计算问题;第二部分是关于Logistic总体分布参数的估计问题;第三部分是关于Logistic总体的拟合优度检验问题。

This thesis has mainly contributed two results in theory: one is that the covariance matrix is not positive definitive if the number of sampling is least than the number of variables, the other is that the covariance matrix of discrete sample is positive definitive if and only if the sum of every random column vector of data matrix is non I-linear combination.

本论文在理论上,主要得出两个新的结果:ⅰ抽样调查中,若样本的个数少于变量的个数则样本协方差矩阵恒为非正定;ⅱ离散型样本协方差矩阵正定的充要条件是样本资料阵的各随机列向量是非I-线性组合。

This result has extended the positive definitiveness of covariance matrix of continuous sample in theory.

从而本论文推广了连续型样本协方差矩阵的正定性。

Traditional multivariable analysis method, for example, principal component analysis method and factor analysis method, are common in calculating the mean vector, the covariance matrix of sample and other variables.

摘要传统的多元统计分析方法,如主成分分析方法和因子分析方法等的共同点是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 thesis discussed the positive definitiveness of the covariance matrix of sample whose variables are discrete data by using p-vector match, I-Linear combination and their properties.

对于变量取离散数据的样本协方差矩阵的正定性,很少有学者进行研究。

The premise of the superiority of the conventional statistical STAP is to have ample training samples with interference that are independently and identically distributed with interference in the test sample, in order to estimate the covariance matrix.

常规的统计STAP方法性能最优的前提条件是具有足够的与待检测样本中的干扰独立同分布的训练样本,以便估计协方差矩阵。

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。

In this dissertation, we develop the research work of the following three aspects based on existed work.First, using the scale-invariant property of multiscale model, i.e. Markovian among scales, a method of qth-order tree-based for multiscale representation of a class of 1-D stochastic process is presented. The multiscale stochastic model is established. The representation forms of parameter matrices, such as, the state transition matrix, the disturbance matrix, the initial state and the corresponding covariance matrix are deduced in detail. The multiscale sample paths based on distinctive order tree are presented by computer simulation.

本文在已有工作的基础上,开展了以下几个方面的研究工作: 1、根据多尺度模型尺度不变性,即利用尺度间的Markov性,给出了一类1-D随机过程基于一般q阶树的多尺度表示方法,建立了相应的多尺度动态模型,详细推导了多尺度模型中的状态转移阵、扰动阵、初始状态和相应的协方差阵,并通过计算机仿真给出了不同阶树的多尺度采样路径。

Based on the previous fault possibility distribution kept within the sample mean and covariance matrix with forgetting factors,a strategy for constructing the target output of the new training sample set was ...

该算法通过带有遗忘因子的样本均值和样本协方差矩阵保存样本所包含的故障可能性分布信息,并在此基础上产生新增样本的目标输出,用于训练FBF网络,以实现故障分类边界的在线跟踪。

First we construct a covariance matrix from sample images, then compute the eigenvalues and corresponding eigenvectors of the covariance matrix, construct a feature matrix with the eigenvectors. Then every images in database can be projected into the feature matrix and gain a projection vector, so does the input image. Then we can judge the resemblance between input image with each image in database by computing the distance between their projection vectors.

我们首先根据采集的样本图像构造一个协方差矩阵,然后求取该矩阵的特征值,以这些矩阵特征值对应的特征向量构造出一个特征空间,然后将输入图像向该特征空间映射,将获取的映射系数与样本库中图像的映射系数进行距离计算,根据计算出的距离判定输入图像与样本图像间的匹配程度。

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