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Bayesian Joint Estimation of Multiple Graphical Models
Lingrui Gan · Xinming Yang · Naveen Narisetty · Feng Liang

Wed Dec 11 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #178
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 the 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 $\ell_\infty$ 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.

Author Information

Lingrui Gan (University of Illinois at Urbana-Champaign)
Xinming Yang (University of Illinois at Urbana-Champaign)
Naveen Narisetty (University of Illinois at Urbana-Champaign)
Feng Liang (Univ. of Illinois Urbana-Champaign)

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