Bayesian Joint Estimation of Multiple Graphical Models

被引：0

作者：

Gan, Lingrui ^{[1
]}

Yang, Xinming ^{[1
]}

Nariestty, Naveen N. ^{[1
]}

Liang, Feng ^{[1
]}

机构：

[1] Univ Illinois, Dept Stat, Champaign, IL 61820 USA

来源：

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019) | 2019年 / 32卷

关键词：

INVERSE COVARIANCE ESTIMATION; VARIABLE SELECTION; INFERENCE; LASSO;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we propose a novel Bayesian group regularization method based on the spike and slab Lasso priors for jointly estimating multiple graphical models. The proposed method can be used to estimate common sparsity structure underlying the graphical models while capturing potential heterogeneity of the precision matrices corresponding to those models. Our theoretical results show that the proposed method enjoys the optimal rate of convergence in l(infinity) norm for estimation consistency and has a strong structure recovery guarantee even when the signal strengths over different graphs are heterogeneous. Through simulation studies and an application to the capital bike-sharing network data, we demonstrate the competitive performance of our method compared to existing alternatives.

引用

页数：11

共 32 条

[1] Bayesian structure learning in graphical models [J].