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单词 Continuous Random Variables
释义

Continuous Random Variables

原声例句
可汗学院:统计学(视频版)

A continuous random variable could take on an infinite number of outcomes.

一个连续的随机变量可以呈现无限多的结果。

可汗学院:统计学(视频版)

The way you would think about a continuous random variable, you could say what is the probability that Y is almost 2?

考虑连续随机变量的方式,您可以说 Y 几乎为 2 的概率是多少?

可汗学院:统计学(视频版)

So a continuous random variable would be the exact-- it could be x is equal to the exact amount of rain in inches tomorrow.

因此,一个连续的随机变量将是精确的——它可能是 x 等于明天以英寸为单位的精确降雨量。

中文百科

概率分布 Probability distribution

(重定向自Continuous Random Variables)

概率分布德语:Wahrscheinlichkeitsverteilung英语:probability distribution)或简称分布,是概率论的一个概念。使用时可以有以下两种含义:

XY为同分布的随机变量,当且仅当对任意事件A \in \mathcal{F} ,有\mathbb{P}(X\in A) = \mathbb{P}(Y\in A)成立。

但是,不能认为同分布的随机变量是相同的随机变量。事实上即使XY同分布,也可以没有任何点ω使得X(ω)=Y(ω)。在这个意义下,可以把随机变量分类,每一类称作一个分布,其中的所有随机变量都同分布。用更简要的语言来说,同分布是一种等价关系,每一个等价类就是一个分布。需注意的是,通常谈到的离散分布、均匀分布、伯努利分布、正态分布、泊松分布等,都是指各种类型的分布,而不能视作一个分布。

英语百科

Probability distribution 概率分布

(重定向自Continuous Random Variables)
The probability mass function (pmf) p(S) specifies the probability distribution for the sum S of counts from two dice.  For example, the figure shows that p(11) = 1/18.  The pmf allows the computation of probabilities of events such as P(S > 9) = 1/12 + 1/18 + 1/36 = 1/6, and all other probabilities in the distribution.
The probability density function (pdf) of the normal distribution, also called Gaussian or
The probability mass function of a discrete probability distribution. The probabilities of the singletons 1, 3, and 7 are respectively 0.2, 0.5, 0.3. A set not containing any of these points has probability zero.

In probability and statistics, a probability distribution assigns a probability to each measurable subset of the possible outcomes of a random experiment, survey, or procedure of statistical inference. Examples are found in experiments whose sample space is non-numerical, where the distribution would be a categorical distribution; experiments whose sample space is encoded by discrete random variables, where the distribution can be specified by a probability mass function; and experiments with sample spaces encoded by continuous random variables, where the distribution can be specified by a probability density function. More complex experiments, such as those involving stochastic processes defined in continuous time, may demand the use of more general probability measures.

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