The size distribution for Markov equivalence classes of acyclic digraph models.

DOI10.1016/S0004-3702(02)00264-3zbMath1043.68096OpenAlexW2055626750MaRDI QIDQ1852851

Steven B. Gillispie, Michael D. Perlman

Publication date: 21 January 2003

Published in: Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0004-3702(02)00264-3

zbMATH Keywords

Bayesian networkMarkov equivalence classGraphical Markov modelAcyclic digraphDagEssential graphGraph countingParent bound

Mathematics Subject Classification ID

Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence (68T35)

Related Items

Learning Markov equivalence classes of directed acyclic graphs: an objective Bayes approach, An efficient algorithm for counting Markov equivalent DAGs, Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs, Bayesian inference of causal effects from observational data in Gaussian graphical models, Bayesian Model Selection of Gaussian Directed Acyclic Graph Structures, Equivalence class selection of categorical graphical models, Formulas for counting acyclic digraph Markov equivalence classes, Characterizing Markov equivalence classes for AMP chain graph models, Learning Bayesian networks in the space of structures by estimation of distribution algorithms, Functions of random walks on hyperplane arrangements, Uniform random generation of large acyclic digraphs, Objective Bayes model selection of Gaussian interventional essential graphs for the identification of signaling pathways, A review of Gaussian Markov models for conditional independence, Learning Bayesian networks from big data with greedy search: computational complexity and efficient implementation

Uses Software

• MIM

Cites Work

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• Generating all the acyclic orientations of an undirected graph
• A Bayesian method for the induction of probabilistic networks from data
• A characterization of Markov equivalence classes for acyclic digraphs
• Bayesian analysis in expert systems. With comments and a rejoinder by the authors
• Efficient coding of labeled directed acyclic graphs
• A graphical characterization of the largest chain graphs
• Kombinatorische Anzahlbestimmungen in Relationen
• Bayesian model averaging and model selection for markov equivalence classes of acyclic digraphs
• Orderly algorithms for graph generation
• Sequential updating of conditional probabilities on directed graphical structures
• Every one a Winner or how to Avoid Isomorphism Search when Cataloguing Combinatorial Configurations
• Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window
• Independence properties of directed markov fields