Hierarchical Gaussian graphical models: beyond reversible jump

DOI10.1214/12-EJS746zbMath1335.62042MaRDI QIDQ1950899

Yuan Cheng, Alex Lenkoski

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/1354284421

zbMATH Keywords

exchange algorithmsGaussian graphical models\(G\)-Wishart distributionconditional Bayes factors

Mathematics Subject Classification ID

Related Items (10)

Bayesian structure learning in sparse Gaussian graphical models ⋮ Scaling it up: stochastic search structure learning in graphical models ⋮ Nonparametric Bayesian learning of heterogeneous dynamic transcription factor networks ⋮ Unbiased approximation of posteriors via coupled particle Markov chain Monte Carlo ⋮ The G-Wishart Weighted Proposal Algorithm: Efficient Posterior Computation for Gaussian Graphical Models ⋮ An improved modified cholesky decomposition approach for precision matrix estimation ⋮ Bayesian hierarchical modeling of extreme hourly precipitation in Norway ⋮ Accelerating Bayesian Structure Learning in Sparse Gaussian Graphical Models ⋮ Exact formulas for the normalizing constants of Wishart distributions for graphical models ⋮ Loglinear model selection and human mobility

Uses Software

• AMD
• HdBCS

Cites Work

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