Estimation of Means in Graphical Gaussian Models with Symmetries

We study the problem of estimability of means in undirected graphical Gaussian models with symmetry restrictions represented by a coloured graph. Following on from previous studies, we partition the variables into sets of vertices whose corresponding means are restricted to being identical. We find a necessary and sufficient condition on the partition to ensure equality between the maximum likelihood and least-squares estimators of the mean.