Compatible priors for model selection of high-dimensional Gaussian DAGs

DOI10.1214/20-EJS1768zbMath1455.62062OpenAlexW3095433924MaRDI QIDQ2215951

Guido Consonni, Stefano Peluso

Publication date: 15 December 2020

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ejs/1604545291

zbMATH Keywords

graphical modelsstructural learningMarkov equivalence classdirected acyclic graph (DAG)DAG-Wishart prior

Mathematics Subject Classification ID

Bayesian inference (62F15) Statistical ranking and selection procedures (62F07) Probabilistic graphical models (62H22)

Related Items (4)

Network Structure Learning Under Uncertain Interventions ⋮ Complexity analysis of Bayesian learning of high-dimensional DAG models and their equivalence classes ⋮ Equivalence class selection of categorical graphical models ⋮ Discussion to: Bayesian graphical models for modern biological applications by Y. Ni, V. Baladandayuthapani, M. Vannucci and F.C. Stingo

Uses Software

• pcalg

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Parameter priors for directed acyclic graphical models and the characterization of several probability distributions
• Compatibility of prior specifications across linear models
• Hyper Markov laws in the statistical analysis of decomposable graphical models
• A characterization of Markov equivalence classes for acyclic digraphs
• Prior distributions for objective Bayesian analysis
• Learning Markov equivalence classes of directed acyclic graphs: an objective Bayes approach
• Posterior graph selection and estimation consistency for high-dimensional Bayesian DAG models
• Wishart distributions for decomposable graphs
• Objective Bayes Factors for Gaussian Directed Acyclic Graphical Models
• Cholesky Decompositions and Estimation of A Covariance Matrix: Orthogonality of Variance Correlation Parameters
• Bayesian Networks in R
• Objective Bayes Covariate‐Adjusted Sparse Graphical Model Selection
• Jointly Interventional and Observational Data: Estimation of Interventional Markov Equivalence Classes of Directed Acyclic Graphs
• Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs
• Identifiability of Gaussian structural equation models with equal error variances
• The case for objective Bayesian analysis

This page was built for publication: Compatible priors for model selection of high-dimensional Gaussian DAGs