Hyper Markov law in undirected graphical models with its applications

DOI10.1080/03610926.2023.2263111MaRDI QIDQ6597441

Brian Y. Sun, [[Person:6597440|Author name not available (Why is that?)]]

Publication date: 3 September 2024

Published in: Communications in Statistics. Theory and Methods (Search for Journal in Brave)

Mathematics Subject Classification ID

Could not fetch data.

Cites Work

• Title not available (Why is that?)
• Title not available (Why is that?)
• Title not available (Why is that?)
• Bayesian hypothesis testing for Gaussian graphical models: conditional independence and order constraints
• Hyper Markov laws in the statistical analysis of decomposable graphical models
• Bayesian estimation of large precision matrix based on Cholesky decomposition
• Optimal decomposition by clique separators
• Wishart distributions for decomposable covariance graph models
• Finding the minimal set for collapsible graphical models
• Collapsibility and response variables in contingency tables
• Hyper Inverse Wishart Distribution for Non-decomposable Graphs and its Application to Bayesian Inference for Gaussian Graphical Models
• Collapsibility of Conditional Graphical Models
• Decomposable graphical Gaussian model determination
• The Weighted Likelihood Ratio, Linear Hypotheses on Normal Location Parameters
• Sampling decomposable graphs using a Markov chain on junction trees

This page was built for publication: Hyper Markov law in undirected graphical models with its applications

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6597441)