Editing Gumbel distribution (section)

==Random variate generation==
{{further|Non-uniform random variate generation}}

Since the quantile function (inverse [[cumulative distribution function]]), <math>Q(p)</math>, of a Gumbel distribution is given by

:<math>Q(p)=\mu-\beta\ln(-\ln(p)),</math>

the variate <math>Q(U)</math> has a Gumbel distribution with parameters <math>\mu</math> and <math>\beta</math> when the random variate <math>U</math> is drawn from the [[uniform distribution (continuous)|uniform distribution]] on the interval <math>(0,1)</math>.

===Probability paper===
[[File:Gumbel paper.JPG|thumb|320px|A piece of graph paper that incorporates the Gumbel distribution.]]

In pre-software times probability paper was used to picture the Gumbel distribution (see illustration). The paper is based on linearization of the cumulative distribution function <math>F</math> :
: <math> -\ln(-\ln(F)) = \frac{x-\mu}\beta </math>
In the paper the horizontal axis is constructed at a double log scale. The vertical axis is linear. By plotting <math>F</math> on the horizontal axis of the paper and the <math>x</math>-variable on the vertical axis, the distribution is represented by a straight line with a slope 1<math>/\beta</math>. When [[distribution fitting]] software like [[CumFreq]] became available, the task of plotting the distribution was made easier.

=== Gumbel reparameterization tricks ===
In [[machine learning]], the Gumbel distribution is sometimes employed to generate samples from the [[categorical distribution]]. This technique is called "Gumbel-max trick" and is a special example of "[[Reparameterization trick|reparameterization tricks]]".<ref>{{Cite conference |first1=Eric |last1=Jang |first2=Shixiang |last2=Gu |first3=Ben |last3=Poole |date=April 2017 |title=Categorical Reparameterization with Gumble-Softmax |url=https://pure.mpg.de/pubman/faces/ViewItemOverviewPage.jsp?itemId=item_2564872 |conference=International Conference on Learning Representations (ICLR) 2017}}</ref>

In detail, let <math>(\pi_1, \ldots, \pi_n)</math> be nonnegative, and not all zero, and let <math>g_1,\ldots , g_n</math> be independent samples of Gumbel(0, 1), then by routine integration,<math display="block">Pr(j = \arg\max_i (g_i + \log\pi_i)) = \frac{\pi_j}{\sum_i \pi_i}</math>That is, <math>\arg\max_i (g_i + \log\pi_i) \sim \text{Categorical}\left(\frac{\pi_j}{\sum_i \pi_i}\right)_j</math>

Equivalently, given any <math>x_1, ..., x_n\in \R</math>, we can sample from its [[Boltzmann distribution]] by

<math display="block">Pr(j = \arg\max_i (g_i + x_i)) = \frac{e^{x_j}}{\sum_i e^{x_i}}</math>Related equations include:<ref>{{Cite journal |last1=Balog |first1=Matej |last2=Tripuraneni |first2=Nilesh |last3=Ghahramani |first3=Zoubin |last4=Weller |first4=Adrian |date=2017-07-17 |title=Lost Relatives of the Gumbel Trick |url=https://proceedings.mlr.press/v70/balog17a.html |journal=International Conference on Machine Learning |language=en |publisher=PMLR |pages=371–379|arxiv=1706.04161 }}</ref>
* If <math>x\sim \operatorname{Exp}(\lambda)</math>, then <math>(-\ln x - \gamma)\sim \text{Gumbel}(-\gamma + \ln\lambda, 1)</math>.
* <math>\arg\max_i (g_i + \log\pi_i) \sim \text{Categorical}\left(\frac{\pi_j}{\sum_i \pi_i}\right)_j</math>.
* <math>\max_i (g_i + \log\pi_i) \sim \text{Gumbel}\left(\log\left(\sum_i \pi_i \right), 1\right)</math>. That is, the Gumbel distribution is a max-stable distribution family.
* <math>\mathbb E[\max_i (g_i + \beta x_i)] = \log \left(\sum_i e^{\beta x_i}\right) + \gamma.</math>