rxmc.likelihood_model.mahalanobis_distance_sqr_cholesky#

rxmc.likelihood_model.mahalanobis_distance_sqr_cholesky(y, ym, cov)[source]#

Squared Mahalanobis distance and log-determinant via Cholesky factorisation.

Parameters:

  • y (array-like, shape (n,)) – Observation vector.

  • ym (array-like, shape (n,)) – Model prediction vector.

  • cov (array-like, shape (n, n)) – Positive-definite covariance matrix.

Returns:

  • mahalanobis_sqr (float) – $(y - y_m)^T Sigma^{-1} (y - y_m)$.

  • log_det (float) – $log det Sigma$.