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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 effective signal subspace effected on by the angular correlation is analyzed through the array covariance matrix. One distributed source from any degree of angular correlation cannot be generally taken as the independent distributed source.

对于实际应用和理论推导中,源内角度何等相关程度的分布源相关模型可等价于分布源独立或相干模型要做具体分析,不能笼统地认为所有分布源相关模型都用分布源独立模型描述和处理。

We introduce the general sigma point selection framework, and give two kinds of efficient methods: a reducing the computing complexity by decreasing the number of the sigma point; b scaling the sigma points to influence the effects of the higher order moments, then improve the accuracy of the UT; c Convergence analysis of the UKF, when used as an observer for nonlinear deterministic discrete-time system, is presented. Based on the UKF that captures the posterior mean and covariance accurately to the second or third order term (in terms of Taylor series expansion) for any form of nonlinearity, sufficient conditions to ensure local asymptotic convergence are established.

论文介绍了sigma点的一般选取规则,并给出了两种有效的方法:a通过减少sigma点集合的元素的数目来减少计算复杂度,b通过增加参数来逼近泰勒展开的高阶项,从而提高UT变换的精度;c论文研究了UKF算法应用于非线性确定性离散时间系统时的收敛性问题。

We discuss asymptotically best linear unbiased estimators of the Logistic population based on selected order statistics and give the formula of computing the ABLUE, the variance and the covariance of the ABLUE in limit, and properties of the ABLUE, and so on. We give the optimum chosen of spacing which is the ABLUE with maximum asymptotic relative efficiency based on complete sample when selected order statistics number is less than 10, and obtain its maximum asymptotic relative efficiency.

讨论基于Logistic总体Ⅱ型截尾样本的若干个样本分位数的总体分布参数的近似最佳线性无偏估计;给出了该近似最佳线性无偏估计的计算公式,估计方差与协方差的极限表达式及估计量的大样本性质等;给出了全样本,样本分位点个数固定,但不超过10情形下,使上述近似最佳线性无偏估计有最大相对联合估计效率时,样本分位数选取法及最大相对联合估计效率;为大样本情形下,该近似最佳线性无偏估计的应用提供了理论依据。

This section simply introduces the basic principles of the conventional analysis of time series (mean, variance, standard deviation; covariance and correlation coefficient; autocovariance and autocorrelation coefficient), frequency spectrum analysis and wavelet analysis.

本文简单地介绍了传统时序分析(包括平均值、方差和标准差;协方差和相关因子;自协方差和自相关因子)、频谱分析和小波分析的基本原理。

When adopting stochastic software in the solution of base line, it can only obtain the covariance matrix of a single base line vector .

在基线解算采用随机软件时,仅能获得单一基线向量的协方差阵。

In this thesis,using the Bernstein polynomial approximation,Stieltjes transform method and the central limit theorem of martingale,under suitable moment conditions,we prove the CLT of LSS G_n with a generalized regular class C4 of the kernel functions for large dimensional sample covariance matrices.

在适当的矩条件下,当核函数f属于C4时,我们证明了大维随机协方差矩阵的线性谱统计量的中心极限定理。

Robust best invariant unbiased estimator, quadratical form, variance/covariance components

稳健最优不变二次无偏估计,二次型,方差和协方差分量

As to seemingly unrelated regression system,a new biased contracting estimator of the parameters is put forward,which is the combination of generalized ridge covariance-improved estimator and Stein estimator,and the good features of this estimator in mean square error is discussed.

对于一类相依回归系统,结合广义岭型协方差改进估计与Ste in估计,提出了一种新的有偏压缩估计,,并讨论了该估计在均方误差下的优良性质

In this paper we discuss the influence problem of Ridge Estimator in general linear regression model, study the influence analysis on Ridge Estimator with covariance matrix disturbance and data deletion, define Cook distance of Rideg Estimator based on biased estimator.

讨论一般线性回归模型岭估计的影响+分析问题,研究了协方差阵扰动和数据删除对岭估计的影响,给出了岭估计基于有偏估计的Cook距离。

Events,Operation and Relation of Sets, Classical Probability, Geometrical Probability , Statistical Stability of a Frequency, Axioms of Probability, Conditional Probability, Total Probability Theorem, Bayes' Rule,Independent Events,Independent Repeated Trials, One Dimensional Random Variables, Discrete Random Variables, Distribution Function of a Random Variables , Continuous Random Variables, Normal Distribution, Distribution of a Function of a Random Variable, Multidimensional Random Variables, Joint Distribution Function, Marginal Distribution Function,Discrete Two—Dimensional Random Variables,Continuous Two—Dimensional Random Variables, Independent Random Variables, Distribution of Functions of Random Variables,Expectation,Variance, Covariance, Coefficient of Correlation, Bivariate Normal Distribution, Law of Large Numbers, The Central Limit Theorems, Sample and Population ,Chi—Squared, T and F Distributions , Sampling Distributions , Point Estimation , Interval Estimation , Testing Hypotheses , A Test of Significance for Parameters in a Single Sample From a Normally Distributed Population , A Test of Significance for Parameters in Two Sample From Normally Distributed Populations .

本课程的主要内容:概率的概念与运算、随机变量及其分布、随机变量的数字特征与极限定理、数理统计的基本概念、估计和检验的基本方法,随机事件与概率随机事件、事件的关系与运算、几何概率、统计概率等,条件概率、全概率公式、贝叶斯公式、事件的独立性、二项概率公式,随机变量的概念、离散型随机变量、随机变量的分布函数、连续型随机变量、随机变量函数的分布,多维随机变量及其分布函数、边缘分布函数、随机变量的独立性、二维随机变量函数的分布,数学期望、方差、协方差和相关系数、大数定律、中心极限定理,总体与样本, X 2-分布、 t-分布和 F-分布,统计量及抽样分布,假设检验的基本概念、单个正态总体参数的显著性检验、两个正态总体参数的显著性检验。

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