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.