以高值为中心的对数正态随机数 [英] Lognormal Random Numbers Centered around a high value

问题描述

我正在尝试使用numpy/scipy从对数正态分布中创建随机数.

I am trying to create random numbers from a lognormal distribution using numpy/scipy.

平均值为2000，σ为800.

The mean is given as 2000 and sigma as 800.

如果我使用numpy.random.lognormal创建平均值(均值= 2000，sigma = 800，size = 10000) 我得到的是非常高或inf的数字.

If I create my random valus using numpy.random.lognormal(mean=2000, sigma=800, size=10000) all I get is very high or inf numbers.

有没有办法解决这个问题?

Is there a way to work around this?

推荐答案

注意:mean和sigma自变量对应于lognormal分布的对数的分布；分布的实际算术平均值为exp(mean + sigma**2/2)，当mean=2000和sigma=800时，在标准双精度浮点中的计算结果为inf.

Be careful: the mean and sigma arguments correspond to the distribution of the log of the lognormal distribution; the actual arithmetic mean of the distribution is exp(mean + sigma**2/2), which evaluates to inf in standard double precision floating point when mean=2000 and sigma=800.

请参阅 http://docs.scipy .org/doc/numpy/reference/genic/numpy.random.lognormal.html#numpy.random.lognormal 和 http://en.wikipedia.org/wiki/Log-normal_distribution 有关更多详细信息.

See http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.lognormal.html#numpy.random.lognormal and http://en.wikipedia.org/wiki/Log-normal_distribution for more details.

这篇关于以高值为中心的对数正态随机数的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！