Bayesian precision and covariance matrix estimation for graphical Gaussian models with edge and vertex symmetries
具有边与顶点对称性的高斯图模型精度及协方差矩阵贝叶斯估计
Graphical Gaussian models with edge and vertex symmetries were introduced by Højsgaard & Lauritzen (2008), who gave an algorithm for computing the maximum likelihood estimate of the precision matrix for such models. In this paper, we take a Bayesian approach to its estimation. We consider only models with symmetry constraints and which thus form a natural exponential family with the precision matrix as the canonical parameter. We identify the Diaconis–Ylvisaker conjugate…