# Contraction of a quasi-Bayesian model with shrinkage priors in precision matrix estimation

@inproceedings{Zhang2021ContractionOA, title={Contraction of a quasi-Bayesian model with shrinkage priors in precision matrix estimation}, author={Ruoyang Zhang and Yi-Bo Yao and Malay Ghosh}, year={2021} }

Currently several Bayesian approaches are available to estimate large sparse precision matrices, including Bayesian graphical Lasso (Wang, 2012), Bayesian structure learning (Banerjee and Ghosal, 2015), and graphical horseshoe (Li et al., 2019). Although these methods have exhibited nice empirical performances, in general they are computationally expensive. Moreover, we have limited knowledge about the theoretical properties, e.g., posterior contraction rate, of graphical Bayesian Lasso and…

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