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Multivariate Generalized Gaussian Distribution: Convexity and Graphical Models
2013
IEEE Transactions on Signal Processing
We consider covariance estimation in the multivariate generalized Gaussian distribution (MGGD) and elliptically symmetric (ES) distribution. The maximum likelihood optimization associated with this problem is non-convex, yet it has been proved that its global solution can be often computed via simple fixed point iterations. Our first contribution is a new analysis of this likelihood based on geodesic convexity that requires weaker assumptions. Our second contribution is a generalized framework
doi:10.1109/tsp.2013.2267740
fatcat:i6yzyjfprbgl5lf44vkwwmos7i