Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Exact Bayesian Structure Discovery in Bayesian Networks
The Journal of Machine Learning Research
Structure learning of Bayesian networks using constraints
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Large-sample learning of bayesian networks is NP-hard
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
DemocraticOP: A Democratic way of aggregating Bayesian network parameters
International Journal of Approximate Reasoning
Finding optimal Bayesian networks using precedence constraints
The Journal of Machine Learning Research
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The problem of finding a Bayesian network structure which maximizes a score function is known as Bayesian network structure learning from data. We study this problem in this paper with respect to a decomposable score function. Solving this problem is known to be NP-hard. Several algorithms are proposed to overcome this problem such as hill-climbing, dynamic programming, branch and bound, and so on. We propose a new branch and bound algorithm that tries to find the globally optimal network structure with respect to the score function. It is an any-time algorithm, i.e., if stopped, it gives the best solution found. Some pruning strategies are applied to the proposed algorithm and drastically reduce the search space. The performance of the proposed algorithm is compared with the latest algorithm which showed better performance to the others, within several data sets. We showed that the new algorithm outperforms the previously best one.