The Inverse G‐Wishart distribution and variational message passing

DOI10.1111/anzs.12339zbMath1521.62092arXiv2005.09876OpenAlexW3207701366MaRDI QIDQ6075128

Matthew P. Wand, Luca Maestrini

Publication date: 20 October 2023

Published in: Australian & New Zealand Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2005.09876

zbMATH Keywords

approximate Bayesian inferencescalable statistical methodologyG-Wishart distributionmean-field variational Bayes

Mathematics Subject Classification ID

Bayesian inference (62F15) Characterization and structure theory for multivariate probability distributions; copulas (62H05) Probabilistic graphical models (62H22)

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Sparse linear mixed model selection via streamlined variational Bayes

Cites Work

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• Bayesian exploratory factor analysis
• Simple marginally noninformative prior distributions for covariance matrices
• Hyper Markov laws in the statistical analysis of decomposable graphical models
• The matrix-F prior for estimating and testing covariance matrices
• Variational message passing for elaborate response regression models
• Exact formulas for the normalizing constants of Wishart distributions for graphical models
• Factor graph fragmentization of expectation propagation
• On the half-Cauchy prior for a global scale parameter
• Wishart distributions for decomposable graphs
• Semiparametric Regression with R
• Advanced Time Series Data Analysis
• Cholesky decomposition of a hyper inverse Wishart matrix
• A Monte Carlo method for computing the marginal likelihood in nondecomposable Gaussian graphical models
• Matrix Algebra
• Prior distributions for variance parameters in hierarchical models (Comment on article by Browne and Draper)