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边缘分布

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If there exist a family of copulas, we could construct any bivariate or multivariate distribution functions of given margins by Sklars theorem and these distributions are always very useful for simulation.

如果我们有一族copula,那么根据Sklar定理,可以构造出任何指定边缘分布的二元或多元联合分布函数,而这些分布函数在建模与模拟方面是非常有用的。

The Gumbel-Hougaard Copula was employed to construct a bivariate joint probability distribution for describing flood peak and flood volume, whose marginal distributions are both the Pearson type Ⅲ.

利用Cumbel-Hougaard Copula构造边缘分布为PⅢ型分布的两变量联合分布,用以描述洪峰和洪量。

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。

At the same time, the simple independent and identically distributed GGD models can be more robust and efficient than subband-dependent GGD models.

研究表明,自然图像多重子带的边缘分布能充分地反映其特征,而广义高斯分布建模在变换域中被认为是对自然图像最逼近和最成功的边缘分布建模方式。

First, the optimal approximating network is presented and the invariance property of the marginal distribution of some variables is studied.

首先解决了在Bayes网络中删除一条弧之后的最优近似网络中的参数赋值问题,并研究了最优近似网络中某些结点子集的边缘分布在近似以后所具有的不变性,给出了边缘分布保持不变的最大结点子集。

In the paper,a probability distribution with t-EGARCH marginal distribution and the mnltivariate gaussian Copula is built.

建立边缘分布为t-EGARCH分布连接函数为多元正态Copula函数的概率分布模型。

Suppose is two-dimension random variable, call the probability distribution of measure X marginal distribution on X for ; the probability distribution of measure Y

Definition 2.10 设是二维随机变量,称分量的概率分布为关于的边缘分布;分量的概率分布为关于的边缘分布

Suppose is two-dimension random variable, call the probability distribution of measuremarginal distribution on for ; the probability distribution of measure Y marginal distribution on Y for .

Definition 3.6 设是二维随机变量,称分量的概率分布为关于的边缘分布;分量的概率分布为关于的边缘分布

Suppose is two-dimension random variable, call the probability distribution of measure X marginal distribution on X for ; the probability distribution of measure Y marginal distribution on Y for .

Definition 设是二维随机变量,称分量的概率分布为关于的边缘分布;分量的概率分布为关于的边缘分布

Suppose is two-dimension random variable, call the probability distribution of measuremarginal distribution on for ; the probability distribution of measure Y marginal

Definition 3.6 设是二维随机变量,称分量的概率分布为关于的边缘分布;分量的概率分布为关于的边缘分布

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