Vinecop.simulate
- Vinecop.simulate(self: pyvinecopulib.Vinecop, n: int, qrng: bool = False, num_threads: int = 1, seeds: list[int] = []) numpy.ndarray[numpy.float64[m, n]]
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 innum_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.