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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.


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makedistCreate probability distribution object
fitdistFit probability distribution object to data
distributionFitterOpen Distribution Fitter app
cdfCumulative distribution function
gatherGather properties of Statistics and Machine Learning Toolbox object from GPU (Since R2020b)
icdfInverse cumulative distribution function
iqrInterquartile range of probability distribution
meanMean of probability distribution
medianMedian of probability distribution
negloglikNegative loglikelihood of probability distribution
paramciConfidence intervals for probability distribution parameters
pdfProbability density function
plotPlot probability distribution object (Since R2022b)
proflikProfile likelihood function for probability distribution
randomRandom numbers
stdStandard deviation of probability distribution
truncateTruncate probability distribution object
varVariance of probability distribution
evcdfExtreme value cumulative distribution function
evpdfExtreme value probability density function
evinvExtreme value inverse cumulative distribution function
evlikeExtreme value negative log-likelihood
evstatExtreme value mean and variance
evfitExtreme value parameter estimates
evrndExtreme value random numbers


ExtremeValueDistributionExtreme 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.