Bayesian structure learning in graphical models
type: Item
instance of: scholarly article
title: Bayesian structure learning in graphical models
zbMATH Open document ID: 1308.62119
author: Sayantan Banerjee
author: Subhashis Ghosal
published in: Journal of Multivariate Analysis
publication date: 2015-03-24T00:00:00Z
Mathematics Subject Classification ID: 62H12
Mathematics Subject Classification ID: 62F12
Mathematics Subject Classification ID: 62F15
zbMATH DE Number: 6419244
zbMATH Keywords: graphical lasso
zbMATH Keywords: graphical models
zbMATH Keywords: Laplace approximation
zbMATH Keywords: posterior convergence
zbMATH Keywords: precision matrix
describes a project that uses: huge
describes a project that uses: HdBCS
describes a project that uses: glasso
MaRDI profile type: Publication
full work available at URL: j.jmva.2015.01.015
OpenAlex ID: W1994598462
Wikidata QID: Q57438006
cites work: A Monte Carlo method for computing the marginal likelihood in nondecomposable Gaussian graphical models
cites work: Q3096116
cites work: Posterior convergence rates for estimating large precision matrices using graphical models
cites work: Covariance regularization by thresholding
cites work: Regularized estimation of large covariance matrices
cites work: Q4821526
cites work: A Constrainedℓ1Minimization Approach to Sparse Precision Matrix Estimation
cites work: Optimal rates of convergence for covariance matrix estimation
cites work: Needles and straw in a haystack: posterior concentration for possibly sparse sequences
cites work: Fast Bayesian model assessment for nonparametric additive regression
cites work: Hyper Markov laws in the statistical analysis of decomposable graphical models
cites work: Sparse graphical models for exploring gene expression data
cites work: Sparse inverse covariance estimation with the graphical lasso
cites work: Asymptotic normality of posterior distributions for exponential families when the number of parameters tends to infinity.
cites work: Convergence rates of posterior distributions.
cites work: Joint estimation of multiple graphical models
cites work: Q3499239
cites work: Covariance matrix selection and estimation via penalised normal likelihood
cites work: Operator norm consistent estimation of large-dimensional sparse covariance matrices
cites work: Sparsistency and rates of convergence in large covariance matrix estimation
cites work: Q4382293
cites work: A well-conditioned estimator for large-dimensional covariance matrices
cites work: Wishart distributions for decomposable graphs
cites work: The nonparanormal: semiparametric estimation of high dimensional undirected graphs
cites work: High-dimensional graphs and variable selection with the Lasso
cites work: The Bayesian Lasso
cites work: Posterior contraction in sparse Bayesian factor models for massive covariance matrices
cites work: Flexible covariance estimation in graphical Gaussian models
cites work: Sparse permutation invariant covariance estimation
cites work: Generalized Thresholding of Large Covariance Matrices
cites work: Cholesky decomposition of a hyper inverse Wishart matrix
cites work: Bayesian graphical Lasso models and efficient posterior computation
cites work: Efficient Empirical Bayes Variable Selection and Estimation in Linear Models
cites work: Model selection and estimation in the Gaussian graphical model
cites work: The huge Package for High-dimensional Undirected Graph Estimation in R
DBLP publication ID: journals/ma/BanerjeeG15
DOI: 10.1016/J.JMVA.2015.01.015
description: scientific article; zbMATH DE number 6419244