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Bayesian inference for high-dimensional decomposable graphs (Q2044345)

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Language	Label	Description	Also known as
English	Bayesian inference for high-dimensional decomposable graphs	scientific article

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scholarly article

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Bayesian inference for high-dimensional decomposable graphs (English)

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Electronic Journal of Statistics

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publication date

9 August 2021

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full work available at URL

https://arxiv.org/abs/2004.08102

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zbMATH Keywords

\(G\)-Wishart prior

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strong graph selection consistency

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posterior convergence rate

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describes a project that uses

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MaRDI profile type

MaRDI publication profile

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A Monte Carlo method for computing the marginal likelihood in nondecomposable Gaussian graphical models

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Posterior convergence rates for estimating large precision matrices using graphical models

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Bayesian structure learning in graphical models

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Optimal predictive model selection.

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A Constrained<i>ℓ</i><sub>1</sub>Minimization Approach to Sparse Precision Matrix Estimation

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Estimating sparse precision matrix: optimal rates of convergence and adaptive estimation

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Estimating structured high-dimensional covariance and precision matrices: optimal rates and adaptive estimation

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Optimal rates of convergence for sparse covariance matrix estimation

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Posterior graph selection and estimation consistency for high-dimensional Bayesian DAG models

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Objective Bayesian model selection in Gaussian graphical models

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Bayesian linear regression with sparse priors

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Sparse inverse covariance estimation with the graphical lasso

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Fundamentals of Nonparametric Bayesian Inference

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Sampling decomposable graphs using a Markov chain on junction trees

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Experiments in stochastic computation for high-dimensional graphical models

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Estimating Large Precision Matrices via Modified Cholesky Decomposition

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Optimal Bayesian minimax rates for unconstrained large covariance matrices

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Minimax posterior convergence rates and model selection consistency in high-dimensional DAG models based on sparse Cholesky factors

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TIGER: A tuning-insensitive approach for optimally estimating Gaussian graphical models

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Empirical Bayes posterior concentration in sparse high-dimensional linear models

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High-dimensional graphs and variable selection with the Lasso

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Flexible covariance estimation in graphical Gaussian models

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High-dimensional covariance estimation by minimizing \(\ell _{1}\)-penalized log-determinant divergence

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Asymptotic normality and optimalities in estimation of large Gaussian graphical models

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Sparse permutation invariant covariance estimation

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Cholesky decomposition of a hyper inverse Wishart matrix

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Hyper Inverse Wishart Distribution for Non-decomposable Graphs and its Application to Bayesian Inference for Gaussian Graphical Models

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Scaling it up: stochastic search structure learning in graphical models

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High dimensional posterior convergence rates for decomposable graphical models

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On the computational complexity of high-dimensional Bayesian variable selection

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Model selection and estimation in the Gaussian graphical model

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Identifiers

zbMATH Open document ID

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10.1214/21-EJS1822

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Mathematics Subject Classification ID

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zbMATH DE Number

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Sitelinks

Mathematics(1 entry)

mardi Publication:2044345

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