CopulaDistribution.rvs(nobs=1, cop_args=None, marg_args=None, random_state=None)[source]

Draw n in the half-open interval [0, 1).

Sample the joint distribution.

nobsint, optional

Number of samples to generate in the parameter space. Default is 1.


Copula parameters. If None, then the copula parameters will be taken from the cop_args attribute created when initiializing the instance.

marg_argslist of tuples

Parameters for the marginal distributions. It can be None if none of the marginal distributions have parameters, otherwise it needs to be a list of tuples with the same length has the number of marginal distributions. The list can contain empty tuples for marginal distributions that do not take parameter arguments.

random_state{None, int, numpy.random.Generator}, optional

If seed is None then the legacy singleton NumPy generator. This will change after 0.13 to use a fresh NumPy Generator, so you should explicitly pass a seeded Generator if you need reproducible results. If seed is an int, a new Generator instance is used, seeded with seed. If seed is already a Generator instance then that instance is used.

samplearray_like (n, d)

Sample from the joint distribution.


The random samples are generated by creating a sample with uniform margins from the copula, and using ppf to convert uniform margins to the one specified by the marginal distribution.

Last update: Jun 14, 2024