Vinecop.gradient

Vinecop.gradient(self, u: numpy.ndarray, step_wise: bool = True, num_threads: int = 1, parameters: numpy.ndarray | None = None) → numpy.ndarray

Evaluates the gradient of the average log-likelihood.

Returns the observation-average of scores() as a vector of length npars, mirroring how hessian() averages hessian_full().

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.