Asymptotic Bayesian structure learning using graph supports for Gaussian graphical models

DOI10.1016/j.jmva.2005.08.008zbMath1099.62025OpenAlexW1976339100MaRDI QIDQ2507765

Habib Benali, Guillaume Marrelec

Publication date: 5 October 2006

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

Full work available at URL: https://doi.org/10.1016/j.jmva.2005.08.008

zbMATH Keywords

Bayesian analysisGibbs samplerGaussian graphical modelspartial correlation coefficientsconditional independence graphsHIV study data

Mathematics Subject Classification ID

Measures of association (correlation, canonical correlation, etc.) (62H20) Bayesian inference (62F15) Characterization and structure theory for multivariate probability distributions; copulas (62H05) Applications of graph theory (05C90) Graphs and linear algebra (matrices, eigenvalues, etc.) (05C50)

Related Items (1)

Building hyper Dirichlet processes for graphical models

Uses Software

• BayesDA

Cites Work

• Hyper Markov laws in the statistical analysis of decomposable graphical models
• Connection between semidefinite relaxations of the max-cut and stable set problems
• On the sparsity order of a graph and its deficiency in chordality
• On a positive semidefinite relaxation of the cut polytope
• The Isserlis matrix and its application to non-decomposable graphical Gaussian models
• Numerical Bayesian Methods Applied to Signal Processing
• Decomposable graphical Gaussian model determination
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