The size distribution for Markov equivalence classes of acyclic digraph models
                    
                        
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The size distribution for Markov equivalence classes of acyclic digraph models
Bayesian networks, equivalently graphical Markov models determined by acyclic digraphs or ADGs (also called directed acyclic graphs or dags), have proved to be both effective and efficient for representing complex multivariate dependence structures in terms of local relations. However, model search and selection is potentially complicated by the many-to-one correspondence between ADGs and the s...
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Graphical Markov models determined by acyclic digraphs (ADGs), also called directed acyclic graphs (DAOs), are widely studied in statistics, computer science (as Bayesian networks), operations research (as influence diagrams), and many related fields. Because different ADOs may determine the same Markov equivalence class, it long has been of interest to determine the efficiency gained in model ...
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The sizes of Markov equivalence classes of directed acyclic graphs play important roles in measuring the uncertainty and complexity in causal learning. A Markov equivalence class can be represented by an essential graph and its undirected subgraphs determine the size of the class. In this paper, we develop a method to derive the formulas for counting the sizes of Markov equivalence classes. We ...
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Indiana University and the University of Washington 2Chain graphs (CG) (= adicyclic graphs) use undirected and directed edges to represent simultaneously both structural and associative dependences.. Like acyclic directed graphs (ADGs), the CG associated with a given statistical model may not be unique, so CGs fall into Markov equivalence classes, which may be superexponentially large, leading ...
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ژورنال
عنوان ژورنال: Artificial Intelligence
سال: 2002
ISSN: 0004-3702
DOI: 10.1016/s0004-3702(02)00264-3