def _logpdf(self, x, mean, prec_U, log_det_cov, rank):
dev = x - mean<EOL>maha = np.sum(np.square(np.dot(dev, prec_U)), axis=-<NUM_LIT:1>)<EOL>return -<NUM_LIT:0.5> * (rank * _LOG_2PI + log_det_cov + maha)<EOL>
Parameters
----------
x : ndarray
    Points at which to evaluate the log of the probability
    density function
mean : ndarray
    Mean of the distribution
prec_U : ndarray
    A decomposition such that np.dot(prec_U, prec_U.T)
    is the precision matrix, i.e. inverse of the covariance matrix.
log_det_cov : float
    Logarithm of the determinant of the covariance matrix
rank : int
    Rank of the covariance matrix.

Notes
-----
As this function does no argument checking, it should not be
called directly; use 'logpdf' instead.