A Metropolis-Hastings based method for sampling from the \(G\)-Wishart distribution in Gaussian graphical models

DOI10.1214/11-EJS594zbMath1274.65009OpenAlexW2000045933WikidataQ105531941 ScholiaQ105531941MaRDI QIDQ1952170

Hélène Massam, Michael D. Escobar, Nicholas Mitsakakis

Publication date: 28 May 2013

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ejs/1295457468

zbMATH Keywords

Metropolis-Hastings algorithmGaussian graphical modelsnon-decomposable graphs\(G\)-Wishart distributiondeviance information criterion

Mathematics Subject Classification ID

Related Items (12)

Bayesian state space models for dynamic genetic network construction across multiple tissues ⋮ A Bayesian Subset Specific Approach to Joint Selection of Multiple Graphical Models ⋮ High dimensional posterior convergence rates for decomposable graphical models ⋮ Efficient Gaussian graphical model determination under \(G\)-Wishart prior distributions ⋮ Hierarchical Gaussian graphical models: beyond reversible jump ⋮ Simulation of hyper-inverse Wishart distributions for non-decomposable graphs ⋮ Exact formulas for the normalizing constants of Wishart distributions for graphical models ⋮ Bayesian graphical models for differential pathways ⋮ Modeling correlated marker effects in genome-wide prediction via Gaussian concentration graph models ⋮ Bayesian method for causal inference in spatially-correlated multivariate time series ⋮ Bayesian Approaches for Large Biological Networks ⋮ Statistical inference of reliability of generalized Rayleigh distribution under progressively type-II censoring

Uses Software

• BayesDA

Cites Work

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• Conjugate priors for exponential families
• Markov chains for exploring posterior distributions. (With discussion)
• Rates of convergence of the Hastings and Metropolis algorithms
• Simulation of hyper-inverse Wishart distributions for non-decomposable graphs
• Hyper Inverse Wishart Distribution for Non-decomposable Graphs and its Application to Bayesian Inference for Gaussian Graphical Models
• Bayesian Measures of Model Complexity and Fit
• Cholesky decomposition of a hyper inverse Wishart matrix
• Independence Structure of Natural Conjugate Densities to Exponential Families and the Gibbs' Sampler
• Simulation of hyper-inverse Wishart distributions in graphical models
• A Monte Carlo method for computing the marginal likelihood in nondecomposable Gaussian graphical models

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