Probabilistic reasoning in intelligent systems: networks of plausible inference
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Equivalence and synthesis of causal models
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Learning mixtures of DAG models
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A transformational characterization of equivalent Bayesian network structures
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Learning equivalence classes of Bayesian network structures
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Knowledge-Based operations for graphical models in planning
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Review: learning bayesian networks: Approaches and issues
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In this paper, we present a heuristic operator which aims at simultaneously optimizing the orientations of all the edges in an intermediate Bayesian network structure during the search process. This is done by alternating between the space of directed acyclic graphs (DAGs) and the space of skeletons. The found orientations of the edges are based on a scoring function rather than on induced conditional independences. This operator can be used as an extension to commonly employed search strategies. It is evaluated in experiments with artificial and real-world data.