协方差

In order to solve the problem how to construct covariance matrix that used prescient information of measurement data in least-square-method,a cellular localization algorithm based on the BP neural network is proposed.

为了解决最小二乘法需要测量数据的先验信息来构造协方差矩阵的问题，提出了基于BP神经网络的蜂窝无线定位算法。

Furthermore,singular value decompositionis applied in the calculation of the covariance matrix of the formulas of Kalman Filter.

同时 ，在卡尔曼滤波计算中，将奇异值分解应用于卡尔曼滤波的递推公式中，提高了协方差阵计算的数值稳定性。

In order to obtain the weights of WWFM, an approach named Covariance Matrix Method is proposed to estimate the SNR of sources.

为获得该算法中的信噪比权值估计，提出估计目标源信噪比的"协方差矩阵方法"，获得较好的估计效果。

In order to solve the problem how to construct covariance matrix that used prescient information of measurement data in least-square-method, a cellular location algorithm based on the RBF neural network is proposed.

为了解决最小二乘法需要测量数据的先验信息来构造协方差矩阵的问题，提出了基于RBF神经网络的蜂窝无线定位算法。

Based on the high-frequency data, the realized volatility and realized covariance matrix was introduced systematically.

系统介绍基于高频交易数据的"已实现"波动率及由它拓展而来的"已实现"协方差矩阵。

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 paper focus on establishing a set of self-contained theory system on robust estimation by changing the covariance matrix of observations.

本文致力于建立一套基于调整观测量协方差阵的稳健最小二乘估计理论。

Significance and application of covariance functions are discussed.

对协方差函数的意义和应用做了讨论。

Further using Covariance Structure Model,we make an empirical analysis of these factors.

并采用协方差结构模型分析方法，对这些因素进行了实证研究。

The Covariance Structure Model is used to make an empirical analysis of these factors.

并采用协方差结构模型分析方法，对这些因素进行了实证研究。

The split between the two groups can hardly be papered over.

这两个团体间的分歧难以掩饰。

This approach not only encourages a greater number of responses, but minimizes the likelihood of stale groupthink.

这种做法不仅鼓励了更多的反应，而且减少跟风的可能性。