方差矩阵

Parameter identification Markov method for continuous stochastic linear system and bilinear system via wavelet transform The statistical properties of continuous Wiener process are studied.

基于小波变换的离散随机线性和双线性系统参数辨识Markov方法分析了由白噪声驱动的有理滤波器输出随机信号在小波变换下的滤波和去相关特性，提出一种在线估计噪声协方差矩阵及其Cholesky分解因子的方法。

A new non-linear quadratic programming Bayesian prestack three-terms inversion method is developed; Firstly, this method is based on Bayesian parameter estimation theory, Gaussian distribution is used for likelihood function and modified Cauchy distribution is used for prior distribution; Secondly, covariance matrix is used to describe the degree of correlation between the parameters; rock physics relations are used to constrain the inversion results; at last, this method is transformed into non-linear quadratic programming problem, and inversion results are acquired under several constraints.

本文提出了一种基于非线性二次规划的叠前三参数反演方法。首先基于贝叶斯参数估计理论，假设似然函数服从高斯分布，并使待反演的参数服从于改进的Cauchy分布，从而提高了反演结果的分辨率；其次用协方差矩阵来描述参数间的相关程度，进一步提高了反演结果的稳定性；最后将问题转化为一个非线性二次规划的求解问题，并在多种约束下得到问题的解。

Finally, the difference of the positive definite conditions for discrete and continuous sample covariance matrix is discussed.

并通过比较两者的正定性条件，总结出离散型与连续型样本协方差矩阵的不同之处。

Just include inverse of covariance matrix for the training data you used.

只要包括协方差矩阵的逆的训练数据，您使用。

An advantage of this method is that it can be applied when the covariance matrix of the invested projects is positive semi-definite.

该方法的优点在于能够处理各投资项目之间的协方差矩阵为半正定的情形。

The impact of reduced dimension transformation on interference covariance matrix is analyzed.

分析了降维变换对干扰协方差矩阵杂波自由度的影响。

The whole algorithm does not need computing covariance matrix and eigen-decomposition, so the complexity is greatly decreased.

整个算法不需要协方差矩阵的计算和特征值的分解，具有较低的计算复杂度。

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。

In chapter 6, we discuss the adaptive beamforming method when the covariance matrix with desired signal present.

第六章探讨了协方差矩阵中含有信号时自适应波束形成方法。

There are three traditional methods of estimating VaR: Covariance matrix, History simulation and Monte Carlo simulation.

估计VaR的传统方法有三种：协方差矩阵法、历史模拟法和蒙特仁洛模拟法。

In the United States, chronic alcoholism and hepatitis C are the most common ones.

在美国，慢性酒精中毒，肝炎是最常见的。

If you have any questions, you can contact me anytime.

如果有任何问题，你可以随时联系我。