A Metropolis-Hastings based method for sampling from the \(G\)-Wishart distribution in Gaussian graphical models
type: Item
instance of: scholarly article
title: A Metropolis-Hastings based method for sampling from the \(G\)-Wishart distribution in Gaussian graphical models
zbMATH Open document ID: 1274.65009
DOI: 10.1214/11-EJS594
author: Nicholas Mitsakakis
author: Hélène Massam
author: Michael D. Escobar
published in: Electronic Journal of Statistics
publication date: 2013-05-28T00:00:00Z
full work available at URL: https://projecteuclid.org/euclid.ejs/1295457468
Mathematics Subject Classification ID: 65C60
zbMATH DE Number: 6166810
zbMATH Keywords: Gaussian graphical models
zbMATH Keywords: \(G\)-Wishart distribution
zbMATH Keywords: Metropolis-Hastings algorithm
zbMATH Keywords: non-decomposable graphs
zbMATH Keywords: deviance information criterion
describes a project that uses: BayesDA
MaRDI profile type: Publication
cites work: Q3608244
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cites work: Rates of convergence of the Hastings and Metropolis algorithms
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cites work: Hyper Inverse Wishart Distribution for Non-decomposable Graphs and its Application to Bayesian Inference for Gaussian Graphical Models
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cites work: Markov chains for exploring posterior distributions. (With discussion)
cites work: Simulation of hyper-inverse Wishart distributions for non-decomposable graphs
Wikidata QID: Q105531941
OpenAlex ID: W2000045933
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description: scientific article; zbMATH DE number 6166810