问题描述
当您在大多数语言中使用random(min，max)函数时，分布是什么样的?
when you use the random(min,max) function in most languages, what is the distribution like ?
如果我想在20％的时间内生成一个范围内的数字，而在80％的时间内生成另一个范围内的数字，我该如何生成紧随其后的一系列随机数呢?
what if i want to produce a range of numbers for 20% of the time, and another range of numbers for 80% of the time, how can i generate series of random number that follows that ?
ex)我应该获得随机频率，但是"1"的频率必须比"0"的频率高20％左右
ex) i should get random frequency but the frequency of "1" must be higher by around 20% than the frequency of "0"
推荐答案
就像任何人所说的那样，大多数语言上的伪随机数生成器实现了(0,1)上的统一分布.如果您有两个响应类别(0,1)，p的概率为1，则您具有伯努利分布，并且可以用
Like anybody says, pseudo-random number generator on most languages implements the uniform distribution over (0,1).If you have two responses categories (0,1) with p probability for 1, you have a Bernoulli distribution and can be emulated with
# returns 1 with p probability and 0 with (1-p) probability
def bernoulli(p)
rand()<p ? 1:0;
end
就这么简单.偏态正态分布是完全不同的野兽，由正态分布的pdf和cdf的联合"产生偏斜.您可以在此处阅读.使用宝石分布，您可以生成概率密度函数，
Simple as that.Skewed normal distribution is a entirely different beast, made by the 'union' of pdf and cdf of a normal distribution to create the skew. You can read Azzalini's work here. Using gem distribution, you can generate the probability density function, with
# require 'distribution'
def sn_pdf(x,alpha)
sp = 2*Distribution::Normal.pdf(x)*Distribution::Normal.cdf(x*alpha)
end
获得cdf很困难，因为没有解析解决方案，因此您应该进行集成.要从倾斜的法线获取随机数，可以使用接受拒绝算法.
Obtains the cdf is difficult, because there isn't an analytical solution, so you should integrate.To obtain random numbers from a skewed normal, you could use the acceptation-rejection algorithm.
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