We consider multivariate centered Gaussian models for the random vector $(Z^1,\ldots, Z^p)$, whose conditional structure is described by a homogeneous graph and which is invariant under the action of a permutation subgroup. The following paper concerns with model selection within colored graphical Gaussian models, when the underlying conditional dependency graph is known. We derive an analytic expression of the normalizing constant of the Diaconis-Ylvisaker conjugate prior for the precision parameter and perform Bayesian model selection in the class of graphical Gaussian models invariant by the action of a permutation subgroup. We illustrate our results with a toy example of dimension $5$.
翻译:我们考虑的是随机矢量$( ⁇ 1,\ldots, ⁇ p)的多变量中心高斯模型,该模型的有条件结构由同质图形描述,在一个变异分组的动作下是无变的。以下文件涉及在已知基本有条件依赖图形时,在彩色图形高斯模型中选择模式。我们从精确参数之前的 Diaconis-Ylvisaker conjuge 常数中得出一个分析性表达法,并在一个变异分组的动作下,在图形高斯模型类中进行巴耶斯模型选择。我们用一个维度的微小例子来说明我们的结果。