activity
20152025
most citedBayesian precision matrix estimation for graphical Gaussian models with edge and vertex symmetries

2 citations · 2 across the 3 of their papers we have counts for

collaborators
Showing stat.MEShow all

5 papers · 1 filter

stat.ME2025

High-Dimensional Covariate-Dependent Discrete Graphical Models and Dynamic Ising Models

Lyndsay Roach, Qiong Li, Nanwei Wang +1

We propose a covariate-dependent discrete graphical model for capturing dynamic networks among discrete random variables, allowing the dependence structure among vertices to vary w…

stat.ME2020

Penalized composite likelihood for colored graphical Gaussian models

Qiong Li, Xiaoying Sun, Nanwei Wang

This paper proposes a penalized composite likelihood method for model selection in colored graphical Gaussian models. The method provides a sparse and symmetry-constrained estimato…

stat.ME2020

Bayesian model selection approach for colored graphical Gaussian models

Qiong Li, Xin Gao, Helene Massam

We consider a class of colored graphical Gaussian models obtained by placing symmetry constraints on the precision matrix in a Bayesian framework. The prior distribution on the pre…

stat.ME2016

Approximate Bayesian estimation in large coloured graphical Gaussian models

Qiong Li, Xin Gao, Helene Massam

Distributed estimation methods have recently been used to compute the maximum likelihood estimate of the precision matrix for large graphical Gaussian models. Our aim, in this pape…

stat.ME20152 cited

Bayesian precision matrix estimation for graphical Gaussian models with edge and vertex symmetries

Helene Massam, Qiong Li, Xin Gao

Graphical Gaussian models with edge and vertex symmetries were introduced by \citet{HojLaur:2008} who also gave an algorithm to compute the maximum likelihood estimate of the preci…