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Counting and uniform sampling of directed acyclic graphs (DAGs) from a Markov equivalence class are fundamental tasks in graphical causal analysis.
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Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs
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Learning Equivalence Classes of Bayesian-Network Structures
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Counting and Sampling from Markov Equivalent DAGs Using Clique Trees
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Counting and Sampling Markov Equivalent Directed Acyclic Graphs
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LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments
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An Efficient Algorithm for Counting Markov Equivalent DAGs
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