We study two-sample equality testing in Gaussian graphical models. Classical likelihood ratio tests on decomposable graphs admit clique-wise factorizations, offering limited localization and unstable finite-sample behaviour. We propose node-level inference via a leave-one-out Bartlett-adjusted test on a fully connected graph. The resulting increments have standard chi-square null limits, enabling calibrated significance for single nodes and fixed-size subsets. Simulations confirm validity, and a case study shows practical utility.

翻译：本研究探讨高斯图模型中的两样本相等性检验问题。经典似然比检验在可分解图上具有团式分解特性，但其定位能力有限且有限样本行为不稳定。我们通过在完全连通图上构建留一法巴特利特调整检验，提出节点级推断方法。所得增量具有标准卡方零分布极限，从而实现对单个节点及固定大小子集的校准显著性。仿真实验验证了方法的有效性，案例研究展示了其实用价值。