Multivariate Gaussians, semidefinite matrix completion, and convex algebraic geometry
Multivariate Gaussians, semidefinite matrix completion, and convex algebraic geometry
We study multivariate normal models that are described by linear constraints on the inverse of the covariance matrix. Maximum likelihood estimation for such models leads to the problem of maximizing the determinant function over a spectrahedron, and to the problem of characterizing the image of the positive definite cone under …