Cholesky decomposition of a hyper inverse Wishart matrix
From MaRDI portal
Publication:4949543
DOI10.1093/biomet/87.1.99zbMath0974.62047OpenAlexW2033120023MaRDI QIDQ4949543
Publication date: 16 December 2001
Published in: Biometrika (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1093/biomet/87.1.99
Cholesky decompositiondecomposable graphconjugate distributioncovariance selection modelhyper-Markov distributionstrong hyper-Markov property
Characterization and structure theory for multivariate probability distributions; copulas (62H05) Applications of graph theory (05C90) Random matrices (algebraic aspects) (15B52)
Related Items
Bayesian graphical models for modern biological applications, Wishart distributions for decomposable graphs, Unnamed Item, Wishart exponential families on cones related to tridiagonal matrices, Objective methods for graphical structural learning, The Inverse G‐Wishart distribution and variational message passing, High dimensional posterior convergence rates for decomposable graphical models, Wishart distributions for decomposable covariance graph models, Sparse matrix decompositions and graph characterizations, Precision matrix estimation under the horseshoe-like prior-penalty dual, A Metropolis-Hastings based method for sampling from the \(G\)-Wishart distribution in Gaussian graphical models, Bayesian graph selection consistency under model misspecification, Bayesian skew selection for multivariate models, Posterior convergence rates for estimating large precision matrices using graphical models, Enriched conjugate and reference priors for the Wishart family on symmetric cones, Bayesian bandwidth test and selection for high-dimensional banded precision matrices, Bayesian estimation of sparse precision matrices in the presence of Gaussian measurement error, Efficient Bayesian regularization for graphical model selection, Modeling correlated marker effects in genome-wide prediction via Gaussian concentration graph models, Bayesian method for causal inference in spatially-correlated multivariate time series, Bayesian structure learning in graphical models, Constructing priors based on model size for nondecomposable Gaussian graphical models: a simulation based approach, Minimax posterior convergence rates and model selection consistency in high-dimensional DAG models based on sparse Cholesky factors, Efficient local updates for undirected graphical models, Flexible covariance estimation in graphical Gaussian models, On generating random Gaussian graphical models, On the Letac-Massam conjecture and existence of high dimensional Bayes estimators for graphical models, Bayesian inference for high-dimensional decomposable graphs, A review of Gaussian Markov models for conditional independence, Bayesian model determination for multivariate ordinal and binary data