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We study multivariate Gaussian models that are described by linear conditionson the concentration matrix. We compute the maximum likelihood (ML) degrees ofthese models. That is, we count the critical points of the likelihood functionover a linear space of symmetric matrices. We obtain new formulae for the MLdegree, one via Schubert calculus, and another using Segre classes fromintersection theory. We settle the case of codimension one models, andcharacterize the degenerate case when the ML degree is zero.

The Maximum Likelihood Degree of Linear Spaces of Symmetric Matrices