Vinecop.bic

Vinecop.bic(self, u: numpy.ndarray[dtype=float64, shape=(*, *), order='F'] = array([], shape=(0, 0), dtype=float64), num_threads: int = 1) → float

Evaluates the Bayesian information criterion (BIC).

The BIC is defined as

\[\mathrm{BIC} = -2\, \mathrm{loglik} + \log(n) p,\]

where \(\mathrm{loglik}\) is the log-liklihood (see Vinecop.loglik()) and \(p\) is the (effective) number of parameters of the model. The BIC is a consistent model selection criterion for nonparametric models.

Parameters:

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

• 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.

Returns:

The BIC as a double.