Vinecop.scores

Vinecop.scores(self, u: numpy.ndarray, step_wise: bool = True, num_threads: int = 1) → numpy.ndarray

Evaluates the score function.

The score function is defined as the gradient of the log-likelihood with respect to the parameters. This is a thin wrapper around scores_full(); see there for the computational details.

Parameters:

undarray, shape (n, m), dtype float

An \(n \times (d + k)\)or \(n \times 2d\)matrix of evaluation points, where \(k\)is the number of discrete variables (see select()).

step_wisebool

if False, full gradient of the log-likelihood; if True, score function of the step-wise MLE (gradients computed per pair-copula, treating the pseudo-observations as fixed).

num_threadsint

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