depynd.markov_networks._skeptic()

depynd.markov_networks._skeptic(X, lamb, return_precision=False, **kwargs)

Learn the structure of Markov random field with nonparanormal SKEPTIC using Spearman’s rho [liu2012high].

Parameters:

• X (array, shape (n_samples, n_features)) – Observations of variables.
• lamb (float) – Regularization parameter.
• return_precision (bool, default False) – If True, the estimated precision matrix will be returned instead of adjacency matrix.

Returns:

adj – Estimated adjacency matrix (or precision matrix if return_precision is True) of an MRF.

Return type:

array, shape (n_features, n_features)

References

[liu2012high]

Liu, Han, et al. “High-dimensional semiparametric Gaussian copula graphical models.” The Annals of Statistics 40.4 (2012): 2293-2326.