更改

连续概率分布可以用几种方法来描述。'''<font color="#ff8000">概率密度函数 Probability Density Function</font>'''描述了任意给定值的无穷小概率，并且结果在给定区间内的概率可以通过在该区间上积分概率密度函数来计算。可能值位于某一固定区间的概率可以与和收敛于积分的方式有关，因此，连续概率是基于积分的定义。

连续概率分布可以用几种方法来描述。'''<font color="#ff8000">概率密度函数 Probability Density Function</font>'''描述了任意给定值的无穷小概率，并且结果在给定区间内的概率可以通过在该区间上积分概率密度函数来计算。可能值位于某一固定区间的概率可以与和收敛于积分的方式有关，因此，连续概率是基于积分的定义。

[[File:Combined Cumulative Distribution Graphs.png|thumb|455x455px|

[[File:Combined Cumulative Distribution Graphs.png|thumb|455x455px|

左边是概率密度函数。右边是累积分布函数，它是概率密度曲线下面的区域。]]

左边是概率密度函数。右边是累积分布函数，它是概率密度曲线下面的区域。]]

The [[cumulative distribution function]] describes the probability that the random variable is no larger than a given value; the probability that the outcome lies in a given interval can be computed by taking the difference between the values of the cumulative distribution function at the endpoints of the interval. The cumulative distribution function is the [[antiderivative]] of the probability density function provided that the latter function exists. The cumulative distribution function is the area under the [[probability density function]] from minus infinity <math>\infty</math> to <math>x</math> as described by the picture to the right.<ref>{{Cite book|title=A modern introduction to probability and statistics : understanding why and how|date=2005|publisher=Springer|others=Dekking, Michel, 1946-|isbn=978-1-85233-896-1|location=London|oclc=262680588}}</ref>

The [[cumulative distribution function]] describes the probability that the random variable is no larger than a given value; the probability that the outcome lies in a given interval can be computed by taking the difference between the values of the cumulative distribution function at the endpoints of the interval. The cumulative distribution function is the [[antiderivative]] of the probability density function provided that the latter function exists. The cumulative distribution function is the area under the [[probability density function]] from minus infinity <math>\infty</math> to <math>x</math> as described by the picture to the right.<ref>{{Cite book|title=A modern introduction to probability and statistics : understanding why and how|date=2005|publisher=Springer|others=Dekking, Michel, 1946-|isbn=978-1-85233-896-1|location=London|oclc=262680588}}</ref>

累积分布函数指标描述了随机变量不大于给定值的概率; 结果在给定区间内的概率可以通过计算区间终点的累积分布函数差来计算。累积分布函数是概率密度函数的反导函数，前提是后者存在。正如右边图片所描述的那样，累积分布函数是从负无穷到 x 的概率密度函数下面的区域。

累积分布函数指标描述了随机变量不大于给定值的概率; 结果在给定区间内的概率可以通过计算区间终点的累积分布函数差来计算。累积分布函数是概率密度函数的反导函数，前提是后者存在。正如右边图片所描述的那样，累积分布函数是从负无穷到 x 的概率密度函数下面的区域。

图3:The [[probability density function]] (pdf) of the [[normal distribution]], also called Gaussian or "bell curve", the most important continuous random distribution.  As notated on the figure, the probabilities of intervals of values correspond to the area under the curve.

图3:The [[probability density function]] (pdf) of the [[normal distribution]], also called Gaussian or "bell curve", the most important continuous random distribution.  As notated on the figure, the probabilities of intervals of values correspond to the area under the curve.

正态分布的[概率密度函数(pdf) ，也称为高斯或钟形曲线，是最重要的连续随机分布。如图所示，值间隔的概率对应于曲线下面积。]]

正态分布的[概率密度函数(pdf) ，也称为高斯或钟形曲线，是最重要的连续随机分布。如图所示，值间隔的概率对应于曲线下面积。]]