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

`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

ExtremeValueDistribution Extreme value probability distribution object

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.