We present a simple characterization of equivalent Bayesian network structures based on local transformations. The significance of the characterization is twofold. First, we are able to easily prove several new invariant properties of theoretical interest for equivalent structures. Second, we use the characterization to derive an effi\u000ecient algorithm that identifies all of the compelled edges in a structure. Compelled edge identi\ffication is of particular importance for learning Bayesian network structures from data because these edges indicate causal relationships when certain assumptions hold.<\/p>\n","protected":false},"excerpt":{"rendered":"
We present a simple characterization of equivalent Bayesian network structures based on local transformations. The significance of the characterization is twofold. First, we are able to easily prove several new invariant properties of theoretical interest for equivalent structures. 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