边缘分布函数

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。

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。

By the geometric probability model,the intuitionistic method is provided for the marginal density function and conditional probability density function .

利用几何概型得出均匀分布的边缘密度函数和条件概率密度函数的直观求法。

Suppose is the unity density function of , then

分别是关于的边缘分布密度函数。

As the connection of marginal distribution function, the Copula function may not only reflect the relativity of random variable, but also may describe the random variable's related pattern well. Therefore we may use the different Copula function to describe the different related pattern.

作为连接随机变量边缘分布到联合分布的函数，Copula函数不仅可以反映出随机变量间的相关程度，而且可以较好地描述随机变量间的相关模式，因此可以用不同的Copula函数来表示不同的相关模式。

Copulas are functions that join multivariate distribution functions to their one-dimensional marginal distribution functions.

Copula函数是将多维随机变量的联合分布和其边缘分布连接起来的一种函数。

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。

Copulas are functions that joint multivariate distribution functions to their one–dimensional marginal distribution functions.

Copula用来描述多元随机变量的一维边缘分布与其联合分布之间的函数关系。

The advantages of this new method are:(1) The field conductivity distribution function is fitter the actual condition than tradition methods:(2) On the condition of triangle elements cut of field, making the calculation speed increase in large degree than tradition methods.

与单元电导率重建算法相比，节点电导率重建算法的优点有：(1)使场域电导率分布函数更符合实际情况，克服了传统算法中对电导率作离散化处理的缺陷；(2)当场域采用三角形单元剖分方式时，在场域的剖分密度保持不变的情况下，可以大大提高重建计算的速度；(3)在场域采用三角形单元剖分方式时，场域的剖分密度可以比以前加大一倍，在不降低重建计算速度的前提下，可以改善重建计算的精度；(4)由于重建的是所有节点的电导率，避免了先前单元电导率重建图像中"阶梯"状边缘和"锯齿"状边缘的出现，明显地改善了图像的视觉效果。

Copula-EVT model is an exploration to risk management using multivariate estreme thory. Copula is a function which links the multivariate distributions to given marginals.

Copula-EVT模型是对利用多维极值理论研究金融风险的一种探讨，Copula函数是把多个随机变量的联合分布与它们各自的边缘分布相连接的一个函数。

But we don't care about Battlegrounds.

但我们并不在乎沙场中的显露。

Ah! don't mention it, the butcher's shop is a horror.

啊！不用提了。提到肉，真是糟透了。