Learning Markov equivalence classes of directed acyclic graphs: an objective Bayes approach

DOI10.1214/18-BA1101zbMath1407.62189OpenAlexW2791586967WikidataQ130120738 ScholiaQ130120738MaRDI QIDQ1631609

Guido Consonni, Stefano Peluso, Federico Castelletti, Marco L. Della Vedova

Publication date: 6 December 2018

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

Full work available at URL: https://projecteuclid.org/euclid.ba/1521079250

zbMATH Keywords

Bayesian model selectiongraphical modelfractional Bayes factoressential graphCPDAG

Mathematics Subject Classification ID

Multivariate analysis (62H99) Estimation in multivariate analysis (62H12) Bayesian inference (62F15) Directed graphs (digraphs), tournaments (05C20)

Related Items (6)

Bayesian graphical models for modern biological applications ⋮ Objective methods for graphical structural learning ⋮ Network Structure Learning Under Uncertain Interventions ⋮ Complexity analysis of Bayesian learning of high-dimensional DAG models and their equivalence classes ⋮ Compatible priors for model selection of high-dimensional Gaussian DAGs ⋮ Objective Bayes model selection of Gaussian interventional essential graphs for the identification of signaling pathways

Uses Software

• glasso
• TETRAD

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