The Isserlis matrix and its application to non-decomposable graphical Gaussian models

DOI10.1093/biomet/85.3.711zbMath0946.62031OpenAlexW1971609980MaRDI QIDQ4216699

Joe Whittaker, Alberto Roverato

Publication date: 26 October 2000

Published in: Biometrika (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/ce657305a17792128a8b4464ce55011480a0201f

zbMATH Keywords

matrix completionBayes factorconjugate priorhyper-Markov lawgraphical Gaussian modelsIsserlis matrix

Mathematics Subject Classification ID

Bayesian inference (62F15) Applications of graph theory (05C90) Basic linear algebra (15A99)

Related Items

Algebraic factor analysis: tetrads, pentads and beyond, Covariance chains, A loss‐based prior for Gaussian graphical models, Computationally Efficient Learning of Gaussian Linear Structural Equation Models with Equal Error Variances, Building hyper Dirichlet processes for graphical models, Multiple testing and error control in Gaussian graphical model selection, Asymptotic Bayesian structure learning using graph supports for Gaussian graphical models, Hotelling's \(T^2\) tests in paired and independent survey samples: an efficiency comparison, A review of Gaussian Markov models for conditional independence