Empirical Distribution

Fit, evaluate, and generate random samples from an empirical distribution

Statistics and Machine Learning Toolbox™ offers several ways to work with the empirical distribution.

  • Create a probability distribution object EmpiricalDistribution by fitting a probability distribution to sample data. Then, use object functions to evaluate the distribution, generate random numbers, and so on.

  • Use distribution-specific function ecdf.

  • Use generic distribution functions (cdf, icdf, pdf, random) with a specified distribution name ("Empirical") and parameters.

To learn about the empirical distribution, see Nonparametric and Empirical Probability Distributions.

Objects

EmpiricalDistributionEmpirical probability distribution object (Since R2025a)

Functions

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Create EmpiricalDistribution Object

fitdistFit probability distribution object to data

Work with EmpiricalDistribution Object

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
pdfProbability density function
plotPlot probability distribution object (Since R2022b)
randomRandom numbers
stdStandard deviation of probability distribution
truncateTruncate probability distribution object
varVariance of probability distribution
ecdfEmpirical cumulative distribution function

Topics