Vinecop.inverse_rosenblatt

Vinecop.inverse_rosenblatt(self: pyvinecopulib.Vinecop, u: numpy.ndarray[numpy.float64[m, n]], num_threads: int = 1) → numpy.ndarray[numpy.float64[m, n]]

Evaluates the inverse Rosenblatt transform.

The inverse Rosenblatt transform can be used for simulation: the function applied to independent uniform variates resembles simulated data from the vine copula model.

If the problem is too large, it is split recursively into halves (w.r.t. \(n\), the number of observations). “Too large” means that the required memory will exceed 1 GB. An examplary configuration requiring less than 1 GB is \(n = 1000\), \(d = 200\).

Only works for continous models.

Parameter u:

An \(n \times d\) matrix of evaluation points.

Parameter num_threads:

The number of threads to use for computations; if greater than 1, the function will be applied concurrently to num_threads batches of u.