# Extreme Value Distribution

Fit, evaluate, and generate random samples from extreme value distribution

Statistics and Machine Learning Toolbox™ offers multiple ways to work with the extreme value distribution.

• Create an `ExtremeValueDistribution` object and use `ExtremeValueDistribution` object functions.

• Use distribution-specific functions with specified distribution parameters. The functions can accept parameters of multiple extreme value distributions.

To learn about the extreme value distribution, see Extreme Value Distribution.

## Functions

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 `makedist` Create probability distribution object `fitdist` Fit probability distribution object to data `distributionFitter` Open Distribution Fitter app
 `cdf` Cumulative distribution function `gather` Gather properties of Statistics and Machine Learning Toolbox object from GPU (Since R2020b) `icdf` Inverse cumulative distribution function `iqr` Interquartile range of probability distribution `mean` Mean of probability distribution `median` Median of probability distribution `negloglik` Negative loglikelihood of probability distribution `paramci` Confidence intervals for probability distribution parameters `pdf` Probability density function `plot` Plot probability distribution object (Since R2022b) `proflik` Profile likelihood function for probability distribution `random` Random numbers `std` Standard deviation of probability distribution `truncate` Truncate probability distribution object `var` Variance of probability distribution
 `evcdf` Extreme value cumulative distribution function `evpdf` Extreme value probability density function `evinv` Extreme value inverse cumulative distribution function `evlike` Extreme value negative log-likelihood `evstat` Extreme value mean and variance `evfit` Extreme value parameter estimates `evrnd` Extreme value random numbers

## Objects

 `ExtremeValueDistribution` Extreme value probability distribution object

## Topics

• Extreme Value Distribution

Extreme value distributions are often used to model the smallest or largest value among a large set of independent, identically distributed random values representing measurements or observations.