On the prior and posterior distributions used in graphical modelling

DOI10.1214/13-BA819zbMath1329.62145arXiv1201.4058MaRDI QIDQ908014

Marco Scutari

Publication date: 2 February 2016

Published in: Bayesian Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1201.4058

zbMATH Keywords

Bayesian networksrandom graphsstructure learningMarkov networksmultivariate discrete distributions

Mathematics Subject Classification ID

Multivariate analysis (62H99) Bayesian inference (62F15) Characterization and structure theory for multivariate probability distributions; copulas (62H05) Random graphs (graph-theoretic aspects) (05C80) Research exposition (monographs, survey articles) pertaining to statistics (62-02) Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence (68T35) Graphical methods in statistics (62A09)

Related Items (4)

Bayesian structure learning in sparse Gaussian graphical models ⋮ An improved modified cholesky decomposition approach for precision matrix estimation ⋮ Block Structured Graph Priors in Gaussian Graphical Models ⋮ A review of Gaussian Markov models for conditional independence

This page was built for publication: On the prior and posterior distributions used in graphical modelling