Vinecop.mbicv
- Vinecop.mbicv(self, u: numpy.ndarray = array([], shape=(0, 0), dtype=float64), psi0: float = 0.9, num_threads: int = 1) float
Evaluates the modified Bayesian information criterion for vines (mBICV).
The mBICV is defined as
\[\mathrm{mBICV} = -2\, \mathrm{loglik} + \log(n) p, - 2 * \sum_{t=1}^(d - 1) \{q_t \log(\psi_0^t) - (d - t - q_t) \log(1 -\psi_0^t)\},\]where \(\mathrm{loglik}\)is the log-liklihood, \(p\)is the (effective) number of parameters of the model, \(t\)is the tree level, \(\psi_0\)is the prior probability of having a non-independence copula in the first tree, and \(q_t\)is the number of non-independence copulas in tree \(t\); The vBIC is a consistent model selection criterion for parametric sparse vine copula models when \(d = o(\sqrt{n \log n})\).
- 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()orpdf()).- psi0float
Baseline prior probability of a non-independence copula.
- num_threadsint
The number of threads to use for computations; if greater than 1, the function will be applied concurrently to
num_threadsbatches ofu.
- Returns:
- float
The mBICV as a double.