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单词 Discrete distribution
释义

Discrete distribution

原声例句
可汗学院:统计学(视频版)

And this also applies to the discrete probability distributions.

这也适用于离散概率分布。

可汗学院:统计学(视频版)

So since we're sampling from a discrete distribution it's actually going to be a discrete distribution, but it's going to have 100 possible values.

因此,由于我们从离散分布中采样,它实际上将是一个离散分布,但它将具有 100 个可能的值。

可汗学院:统计学(视频版)

And for a discrete distribution like this, your mean or you're expected value is just going to be the probability weighted sum of the different values that your distribution can take on.

对于这样的离散分布,您的均值或期望值将只是您的分布可以采用的不同值的概率加权和。

可汗学院:统计学(视频版)

It's hard to kind of have a good intuition for a discrete distribution because you really can't take on those values, but it makes sense that the distribution is skewed to the right over here.

很难对离散分布有很好的直觉,因为你真的不能接受这些值,但分布在这里向右倾斜是有道理的。

中文百科

概率分布 Probability distribution

(重定向自Discrete distribution)

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Discrete distribution)
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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更新时间:2025/6/18 5:12:42