本研究针对高斯图模型(Gaussian graphical model)在异常值干扰下估计不稳健的问题，提出基于γ-发散度(γ-divergence)的鲁棒贝叶斯图模型。通过构建与频率学派γ-lasso估计相匹配的后验分布，实现了对逆协方差矩阵(Ω)的自动异常值过滤，并开发了加权贝叶斯自助法 ...

(a) Disease progression can be classified into three states: the normal stage, pre-disease stage and disease stage, with the pre-disease stage representing a critical threshold just before the onset ...

Graphical models provide a robust framework for representing the conditional independence structure between variables through networks, enabling nuanced insight into complex high-dimensional data.

(a) Disease progression can be classified into three states: the normal stage, pre-disease stage and disease stage, with the pre-disease stage representing a critical threshold just before the onset ...

We introduce a class of spatiotemporal models for Gaussian areal data. These models assume a latent random field process that evolves through time with random field convolutions; the convolving fields ...

The Andersson-Madigan-Perlman (AMP) Markov property is a recently proposed alternative Markov property (AMP) for chain graphs. In the case of continuous variables with a joint multivariate Gaussian ...