Efficient Gaussian graphical model determination under \(G\)-Wishart prior distributions

DOI10.1214/12-EJS669zbMath1335.62069MaRDI QIDQ1950810

Hao Wang, Sophia Zhengzi Li

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/1328280902

zbMATH Keywords

Gibbs samplerexchange algorithmsGaussian graphical models\(G\)-Wishartnon-decomposable graphsposterior simulationhyper-inverse Wishartpartial analytic structure

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Estimation in multivariate analysis (62H12) Bayesian inference (62F15) Graphical methods in statistics (62A09)

Related Items (38)

Graph-based multivariate conditional autoregressive models ⋮ Bayesian structure learning in sparse Gaussian graphical models ⋮ Scaling it up: stochastic search structure learning in graphical models ⋮ Bayesian model selection approach for coloured graphical Gaussian models ⋮ Unbiased approximation of posteriors via coupled particle Markov chain Monte Carlo ⋮ Bayesian state space models for dynamic genetic network construction across multiple tissues ⋮ MCMC Algorithms for Posteriors on Matrix Spaces ⋮ The G-Wishart Weighted Proposal Algorithm: Efficient Posterior Computation for Gaussian Graphical Models ⋮ Bayesian graphical models for modern biological applications ⋮ Bayesian Variable Selection for Gaussian Copula Regression Models ⋮ Bayesian modeling of multiple structural connectivity networks during the progression of Alzheimer's disease ⋮ Bayesian analysis of nonparanormal graphical models using rank-likelihood ⋮ Incorporating grouping information into Bayesian Gaussian graphical model selection ⋮ Combining Opinions for Use in Bayesian Networks: A Measurement Error Approach ⋮ Covariance structure estimation with Laplace approximation ⋮ Unnamed Item ⋮ Block Structured Graph Priors in Gaussian Graphical Models ⋮ Bayesian causal inference in probit graphical models ⋮ On a wider class of prior distributions for graphical models ⋮ Hierarchical Gaussian graphical models: beyond reversible jump ⋮ Accelerating Bayesian Structure Learning in Sparse Gaussian Graphical Models ⋮ Bayesian inference in nonparanormal graphical models ⋮ Exact formulas for the normalizing constants of Wishart distributions for graphical models ⋮ Bayesian graphical models for differential pathways ⋮ Bayesian method for causal inference in spatially-correlated multivariate time series ⋮ Loglinear model selection and human mobility ⋮ Structural learning and estimation of joint causal effects among network-dependent variables ⋮ A Bayesian Approach for Estimating Dynamic Functional Network Connectivity in fMRI Data ⋮ Bayesian Approaches for Large Biological Networks ⋮ Efficient local updates for undirected graphical models ⋮ Hierarchical normalized completely random measures for robust graphical modeling ⋮ Different types of Bernstein operators in inference of Gaussian graphical model ⋮ Maximum likelihood and maximum a posteriori estimators for the Riesz probability distribution ⋮ An Expectation Conditional Maximization Approach for Gaussian Graphical Models ⋮ Inequalities on partial correlations in Gaussian graphical models containing star shapes ⋮ Bayesian Regularization for Graphical Models With Unequal Shrinkage ⋮ Estimating heterogeneous gene regulatory networks from zero-inflated single-cell expression data ⋮ Vine copula graphical models in the construction of biological networks

Uses Software

• Algorithm 457
• HdBCS

Cites Work

• Unnamed Item
• Copula Gaussian graphical models and their application to modeling functional disability data
• Flexible covariance estimation in graphical Gaussian models
• Simulation of hyper-inverse Wishart distributions for non-decomposable graphs
• A Metropolis-Hastings based method for sampling from the \(G\)-Wishart distribution in Gaussian graphical models
• Sparse covariance estimation in heterogeneous samples
• Experiments in stochastic computation for high-dimensional graphical models
• Sparse seemingly unrelated regression modelling: applications in finance and econometrics
• Dynamic financial index models: modeling conditional dependencies via graphs
• A double Metropolis–Hastings sampler for spatial models with intractable normalizing constants
• Bayesian Inference for General Gaussian Graphical Models With Application to Multivariate Lattice Data
• Partially Collapsed Gibbs Samplers
• Bayesian analysis of matrix normal graphical models
• Hyper Inverse Wishart Distribution for Non-decomposable Graphs and its Application to Bayesian Inference for Gaussian Graphical Models
• Decomposable graphical Gaussian model determination
• Independence Structure of Natural Conjugate Densities to Exponential Families and the Gibbs' Sampler
• Simulation of hyper-inverse Wishart distributions in graphical models
• Common risk factors in the returns on stocks and bonds
• A Monte Carlo method for computing the marginal likelihood in nondecomposable Gaussian graphical models
• Algorithm 457: finding all cliques of an undirected graph
• Introducing Monte Carlo Methods with R
• Dynamic matrix-variate graphical models

This page was built for publication: Efficient Gaussian graphical model determination under \(G\)-Wishart prior distributions