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.