numpy.random.wald

random.wald(mean, scale, size=None)

从瓦尔德分布或逆高斯分布中提取样本。

当比例接近无穷大时，分布变得更像是高斯分布。一些参考文献声称wald是一个平均值等于1的逆高斯，但这绝不是普遍的。

首先研究了逆高斯分布与布朗运动的关系。1956年，M.C.K.Tweedie使用了逆高斯这个名字，因为覆盖单位距离的时间和覆盖单位时间的距离之间存在着逆关系。

注解

新代码应该使用 wald A方法 default_rng() 请参阅 快速启动 .

参数

mean浮点数或类似浮点数的数组

分布平均值，必须大于0。

scale浮点数或类似浮点数的数组

比例参数，必须大于0。

sizeint或int的元组，可选

输出形状。如果给定的形状是，例如， (m, n, k) 然后 m * n * k 取样。如果尺寸是 None （默认），如果 mean 和 scale 都是标量。否则， np.broadcast(mean, scale).size 取样。

返回

outndarray或scalar

从参数化瓦尔德分布中提取样本。

参见

Generator.wald

应该用于新代码。

笔记

瓦尔德分布的概率密度函数是

System Message: WARNING/2 (P（x；平均值，标度）=\sqrt{\frac{scale}{2\pix^3}}e^)

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如上所述，逆高斯分布首先来自对布朗运动的建模。它也是威布尔的竞争对手，用于可靠性建模和股票收益和利率过程建模。

工具书类

1

Brighton Webs有限公司，Wald发行，https://web.archive.org/web/20090423014010/http://www.brighton-webs.co.uk:80/distributions/wald.asp

2

《逆高斯分布：理论：方法论与应用》，Chhikara，Raj S.与Feeks，J.Leroy出版社，1988年。

3

维基百科，“反向高斯分布”https://en.wikipedia.org/wiki/Inverse_Gaussian_Distribution

实例

从分布中绘制值并绘制柱状图：

>>> import matplotlib.pyplot as plt
>>> h = plt.hist(np.random.wald(3, 2, 100000), bins=200, density=True)
>>> plt.show()

numpy.random.vonmises numpy.random.weibull