Vinecop.simulate

Vinecop.simulate(self, n: int, qrng: bool = False, num_threads: int = 1, seeds: collections.abc.Sequence[int] = []) → numpy.ndarray

Simulates from a vine copula model, see inverse_rosenblatt().

Simulated data is always a continous \(n \times d\)matrix. Sampling from a vine copula model is done by first generating \(n \times d\)uniform random numbers and then applying the inverse Rosenblatt transformation.

Parameters:

nint

Number of observations.

qrngbool

Set to true for quasi-random numbers.

num_threadsint

The number of threads to use for computations; if greater than 1, the function will generate n samples concurrently in num_threads batches.

seedslist of int

Seeds of the random number generator; if empty (default), the random number generator is seeded randomly.

Returns:

ndarray, shape (n, m), dtype float

An \(n \times d\)matrix of samples from the copula model.