def _logpdf(self, x, mean, prec_U, log_det_cov, rank):
        """
        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.

        """
        dev = x - mean
        maha = np.sum(np.square(np.dot(dev, prec_U)), axis=-1)
        return -0.5 * (rank * _LOG_2PI + log_det_cov + maha)
dataset/ETHPy150Open scipy/scipy/scipy/stats/_multivariate.py/multivariate_normal_gen._logpdf/original