On the non-local priors for sparsity selection in high-dimensional Gaussian DAG models

On the non-local priors for sparsity selection in high-dimensional Gaussian DAG models

We consider sparsity selection for the Cholesky factor L of the inverse covariance matrix in high-dimensional Gaussian DAG models. The sparsity is induced over the space of L via non-local priors, namely the product moment (pMOM) prior [Johnson, V., & Rossell, D. (2012). Bayesian model selection...

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Bibliographic Details
Main Authors: Xuan Cao, Fang Yang
Format: Article
Language:English
Published: Taylor & Francis Group 2021-10-01
Series:Statistical Theory and Related Fields
Subjects:
bayesian dag models
non-local priors
high-dimensional data
posterior consistency
graph selection
Online Access:http://dx.doi.org/10.1080/24754269.2021.1963182

Internet

http://dx.doi.org/10.1080/24754269.2021.1963182

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