Covariance hypotheses that are linear in both the covariance and the inverse covariance can be broken down into models involving random vectors with specific structures. These models can either have independent identically distributed random vectors with known covariance structures or with a parametrization given by the Clifford algebra. The researchers have identified the exact distributions of maximum likelihood estimates and likelihood ratio test statistics for these models under the assumption of a normal distribution.