We introduce a mixture-model of beta distributions to identify significant correlations among $P$ predictors when $P$ is large. The method relies on theorems in convex geometry, which we use to show how to control the error rate of edge detection in graphical models. Our `betaMix' method does not require any assumptions about the network structure, nor does it assume that the network is sparse. The results in this article hold for a wide class of data generating distributions that include light-tailed and heavy-tailed spherically symmetric distributions.

翻译：我们采用了贝塔分布的混合模型,以确定美元P$预测器在美元P值较大时的重要关联关系。该方法依靠方形几何学的理论,我们用这些理论来显示如何控制图形模型中边缘探测的误率。我们的“贝塔混合”方法并不要求对网络结构作任何假设,也不假定网络是稀少的。本文章中的结果为包括轻尾和重尾的球形分布在内的广泛的数据生成分布提供了大量数据。