help you out probably one of the more intriguing ones it's like the gradient of the log of the determinant of a matrix with respect to the entries in that matrix is just the inverse of that matrix why does it matter well remember a multivariate Gaussian has that determinant of the covariant main covariance matrix up front they all need to find derivative respect to the entries in the covariance matrix in a maximizing the likelihood and the covariance matrix is a parameter you try to find the right setting of that whole