Simulation of hyper-inverse Wishart distributions for non-decomposable graphs

DOI10.1214/10-EJS591zbMath1329.60008MaRDI QIDQ1952111

Hao Wang, Carlos Marinho Carvalho

Publication date: 27 May 2013

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

Full work available at URL: https://projecteuclid.org/euclid.ejs/1291903546

zbMATH Keywords

non-decomposable graphsposterior simulationjunction treeshyper-inverse Wishart

Mathematics Subject Classification ID

Related Items (13)

The G-Wishart Weighted Proposal Algorithm: Efficient Posterior Computation for Gaussian Graphical Models ⋮ High dimensional posterior convergence rates for decomposable graphical models ⋮ On a wider class of prior distributions for graphical models ⋮ Efficient Gaussian graphical model determination under \(G\)-Wishart prior distributions ⋮ Hierarchical Gaussian graphical models: beyond reversible jump ⋮ A Metropolis-Hastings based method for sampling from the \(G\)-Wishart distribution in Gaussian graphical models ⋮ Sparse covariance estimation in heterogeneous samples ⋮ Bayesian graph selection consistency under model misspecification ⋮ Exact formulas for the normalizing constants of Wishart distributions for graphical models ⋮ Bayesian graphical models for differential pathways ⋮ Bayesian Lasso with neighborhood regression method for Gaussian graphical model ⋮ Bayesian Approaches for Large Biological Networks ⋮ A review of Gaussian Markov models for conditional independence

Cites Work

• Estimation and testing for lattice conditional independence models on Euclidean Jordan algebras
• A Metropolis-Hastings based method for sampling from the \(G\)-Wishart distribution in Gaussian graphical models
• Bayesian Inference for General Gaussian Graphical Models With Application to Multivariate Lattice Data
• Hyper Inverse Wishart Distribution for Non-decomposable Graphs and its Application to Bayesian Inference for Gaussian Graphical Models
• Simulation of hyper-inverse Wishart distributions in graphical models
• A Monte Carlo method for computing the marginal likelihood in nondecomposable Gaussian graphical models
• Introducing Monte Carlo Methods with R
• Unnamed Item

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