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

`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

Fit probability distributions to data

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.