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Copula Distributions and Correlated Samples

Fit parameters of a model of correlated random samples to data, evaluate the distribution, generate serially correlated pseudorandom samples

Statistics and Machine Learning Toolbox™ provides multiple functions with specified distribution parameters for working with copula distributions and correlated samples. For more information, see Copulas: Generate Correlated Samples.


copulacdfCopula cumulative distribution function
copulapdfCopula probability density function
copulaparamCopula parameters as function of rank correlation
copulastatCopula rank correlation
copulafitFit copula to data
copularndCopula random numbers


  • Copulas: Generate Correlated Samples

    Copulas are functions that describe dependencies among variables, and provide a way to create distributions that model correlated multivariate data.

  • Generate Correlated Data Using Rank Correlation

    This example shows how to use a copula and rank correlation to generate correlated data from probability distributions that do not have an inverse cdf function available, such as the Pearson flexible distribution family.

  • Simulating Dependent Random Variables Using Copulas

    This example shows how to use copulas to generate data from multivariate distributions when there are complicated relationships among the variables, or when the individual variables are from different distributions.