二维随机变量

二维随机变量分布的求法

Method of Two-dimensional Random Variable Distribution

连续型二维随机变量线性函数和的概率密度的简单求法

A Simple Solving Method of the Probability Density Function with Respect to the Sum of the Linear Function on the Continuous Two-Dimensional Random Variables

采用二维随机变量的概率密度函数来描述，以便使交变载荷的均值和幅值的变化特征都得到充分的反映。

W-ith probability density function of two dimensional stochastic variable to-describe it , the variable characteristic of mean value and amplitude value in cyclic lode can be perfectly presented .

统计处理结果可作为常均值、变均值、双波法以及二维随机变量疲劳载荷谱的编制依据。同时也适用于疲劳寿命估算。

The results of treatment may serve as the basis of fatigue load spectrum compilation for constant-mean-value , variable-mean-value and double-value methods and two dimensional random variable , and also appropriate to the estimation of fatigue life .

一维与二维连续随机变量的函数的分布

Distribution of continuous random variable of one and two dimentions

二维曲线型随机变量及其分布函数

The Two Dimension Curve Random Variable & its Distribution Function

二维连续型随机变量函数的分布的线积分算法

The Linear Integral calculation Method for the Distribution of Two dimensional Continuous Random Varible Function

二维连续型随机变量和的分布的计算

The Computation Method for the Distribution of a Sum on Two Dimensional Continuous Random Variables

解决二维连续型随机变量函数分布的一种方法

One method to seek the solutions of the distributional function for the continuous stochastic variable of two-dimension

本文章主要介绍二维曲线型随机变量及其分布。

This paper contributes to the discussion of the two dimension curve random variable and the distribution .

本文给出二维离散型随机变量相互独立的定义及与这种判定方法等价的严格证明。

The authors rigorously demonstrated that the definition of two dimensional random variable equals to the decision method .

以二维离散型随机变量为例，给出了离散型随机变量相互独立的几个判别方法，并对其进行了比较。

This paper , taking the two-dimensional separate random variable as an example , produced the mutually independent distinctions method for separate random variable , and made a comparison between them .

对二维连续型随机变量的联合密度函数，在一定要求下变上限积分得到新函数，依据密度函数性质进行规范化，从而构造出一个广义的边缘密度函数。

For the joint density function of 2-D continuous random variables , a new function can be obtained by upper limit-variable integral , and then , by normalization of the new function according to the property of density functions , a general marginal density function was constructed .