Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Computer-based probabilistic-network construction
Computer-based probabilistic-network construction
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Enumerating Markov Equivalence Classes of Acyclic Digraph Models
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
On characterizing Inclusion of Bayesian Networks
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
A transformational characterization of equivalent Bayesian network structures
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Learning equivalence classes of Bayesian network structures
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
MCMC learning of bayesian network models by markov blanket decomposition
ECML'05 Proceedings of the 16th European conference on Machine Learning
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
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The search space of Bayesian Network structures is usually defined as Acyclic Directed Graphs (DAGs) and the search is done by local transformations of DAGs. But the space of Bayesian Networks is ordered with respect to inclusion and it is natural to consider that a good search policy should take this into account. The first attempt to do this (Chickering 1996) was using equivalence classes of DAGs instead of DAGs itself. This approach produces better results but it is significantly slower. We present a compromise between these two approaches. It uses DAGs to search the space in such a way that the ordering by inclusion is taken into account. This is achieved by repetitive usage of local moves within each equivalence class of DAGs. We show that this new approach produces better results than the original DAGs approach without substantial change in time complexity. We present empirical results, within the framework of heuristic search and Markov Chain Monte Carlo, provided through the Alarm dataset.