Posterior convergence rates for high-dimensional precision matrix estimation using \(G\)-Wishart priors

DOI10.1002/sta4.147MaRDI QIDQ6540514

Sayantan Banerjee

Publication date: 16 May 2024

Published in: Stat (Search for Journal in Brave)

zbMATH Keywords

graphical modelsprecision matrix\(G\)-Wishartposterior convergence rate

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Sparse inverse covariance estimation with the graphical lasso
• Posterior convergence rates for estimating large precision matrices using graphical models
• High dimensional posterior convergence rates for decomposable graphical models
• Flexible covariance estimation in graphical Gaussian models
• Sparse graphical models for exploring gene expression data
• Bayesian structure learning in graphical models
• Regularized estimation of large covariance matrices
• High-dimensional graphs and variable selection with the Lasso
• Wishart distributions for decomposable graphs
• Model selection and estimation in the Gaussian graphical model
• Cholesky decomposition of a hyper inverse Wishart matrix
• A Monte Carlo method for computing the marginal likelihood in nondecomposable Gaussian graphical models

This page was built for publication: Posterior convergence rates for high-dimensional precision matrix estimation using \(G\)-Wishart priors