Sampling decomposable graphs using a Markov chain on junction trees
From MaRDI portal
Publication:5411016
DOI10.1093/biomet/ass052zbMath1284.62172arXiv1104.4079OpenAlexW2036280132MaRDI QIDQ5411016
Publication date: 22 April 2014
Published in: Biometrika (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1104.4079
Markov chain Monte Carlomodel determinationgraphical modelsMarkov random fieldsconditional independence graphs
Bayesian inference (62F15) Applications of graph theory (05C90) Numerical analysis or methods applied to Markov chains (65C40)
Related Items (14)
Reciprocal graphical models for integrative gene regulatory network analysis ⋮ Unnamed Item ⋮ Graphical posterior predictive classification: Bayesian model averaging with particle Gibbs ⋮ On a wider class of prior distributions for graphical models ⋮ Bayesian Double Feature Allocation for Phenotyping With Electronic Health Records ⋮ Efficient Bayesian regularization for graphical model selection ⋮ Bayesian Approaches for Large Biological Networks ⋮ Efficient local updates for undirected graphical models ⋮ Structural Markov graph laws for Bayesian model uncertainty ⋮ Post-processing posteriors over precision matrices to produce sparse graph estimates ⋮ Bayesian inference for high-dimensional decomposable graphs ⋮ Inequalities on partial correlations in Gaussian graphical models containing star shapes ⋮ Bayesian learning of weakly structural Markov graph laws using sequential Monte Carlo methods ⋮ Sequential sampling of junction trees for decomposable graphs
This page was built for publication: Sampling decomposable graphs using a Markov chain on junction trees