Quasi-Bayesian estimation of large Gaussian graphical models

DOI10.1016/j.jmva.2019.03.005zbMath1446.62162OpenAlexW2944992025WikidataQ127833476 ScholiaQ127833476MaRDI QIDQ2274970

Yves F. Atchadé

Publication date: 1 October 2019

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2019.03.005

zbMATH Keywords

pseudo-likelihoodGaussian graphical modelsposterior contractionquasi-Bayesian inference

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Martingales with discrete parameter (60G42) Bayesian inference (62F15) Strong limit theorems (60F15) Probabilistic graphical models (62H22)

Related Items (2)

Contraction of a quasi-Bayesian model with shrinkage priors in precision matrix estimation ⋮ A Bayesian Subset Specific Approach to Joint Selection of Multiple Graphical Models

Uses Software

• selectiveInference

Cites Work

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• Bayesian variable selection with shrinking and diffusing priors
• Statistics for high-dimensional data. Methods, theory and applications.
• Bayesian linear regression with sparse priors
• The Bayesian covariance lasso
• On the contraction properties of some high-dimensional quasi-posterior distributions
• Convergence rates of posterior distributions.
• Sparse permutation invariant covariance estimation
• Bayesian structure learning in graphical models
• High-dimensional graphs and variable selection with the Lasso
• Sparse Matrix Inversion with Scaled Lasso
• Reconstruction From Anisotropic Random Measurements
• Bayesian Inference for General Gaussian Graphical Models With Application to Multivariate Lattice Data
• A study of error variance estimation in Lasso regression
• The horseshoe estimator for sparse signals
• The Bayesian Lasso
• Partial Correlation Estimation by Joint Sparse Regression Models
• Bayesian Inference of Multiple Gaussian Graphical Models
• A Monte Carlo method for computing the marginal likelihood in nondecomposable Gaussian graphical models

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