Vinecop.simulate

Vinecop.simulate(self, n: int, qrng: bool = False, num_threads: int = 1, seeds: collections.abc.Sequence[int] = []) → numpy.ndarray[dtype=float64, shape=(*, *), order='F']

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:

  • n – Number of observations.

  • qrng – Set to true for quasi-random numbers.

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

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

Returns:

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