Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, USA.
Department of Statistics, Purdue University, West Lafayette, IN, USA.
Biostatistics. 2021 Apr 10;22(2):233-249. doi: 10.1093/biostatistics/kxz027.
Motivated by the study of the molecular mechanism underlying type 1 diabetes with gene expression data collected from both patients and healthy controls at multiple time points, we propose a hybrid Bayesian method for jointly estimating multiple dependent Gaussian graphical models with data observed under distinct conditions, which avoids inversion of high-dimensional covariance matrices and thus can be executed very fast. We prove the consistency of the proposed method under mild conditions. The numerical results indicate the superiority of the proposed method over existing ones in both estimation accuracy and computational efficiency. Extension of the proposed method to joint estimation of multiple mixed graphical models is straightforward.
受从多个时间点采集的患者和健康对照者的基因表达数据研究 1 型糖尿病分子机制的启发，我们提出了一种混合贝叶斯方法，用于联合估计具有不同条件下观测数据的多个相依高斯图形模型，该方法避免了高维协方差矩阵的求逆，因此可以非常快速地执行。我们在温和条件下证明了所提出方法的一致性。数值结果表明，该方法在估计准确性和计算效率方面均优于现有方法。将所提出的方法扩展到多个混合图形模型的联合估计也很直接。
