Fundamental concepts of qualitative probabilistic networks
Artificial Intelligence
Nonmonotone Spectral Projected Gradient Methods on Convex Sets
SIAM Journal on Optimization
Optimal structure identification with greedy search
The Journal of Machine Learning Research
Exact Bayesian Structure Discovery in Bayesian Networks
The Journal of Machine Learning Research
Large-sample learning of bayesian networks is NP-hard
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Globally optimal structure learning of Bayesian networks from data
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Efficient Structure Learning of Bayesian Networks using Constraints
The Journal of Machine Learning Research
Learning optimal Bayesian networks using A* search
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
LEARNING AND VERIFYING SAFETY CONSTRAINTS FOR PLANNERS IN A KNOWLEDGE-IMPOVERISHED SYSTEM
Computational Intelligence
Characteristic imsets for learning Bayesian network structure
International Journal of Approximate Reasoning
Finding optimal Bayesian networks using precedence constraints
The Journal of Machine Learning Research
Parameterized complexity results for exact bayesian network structure learning
Journal of Artificial Intelligence Research
Learning Bayesian network structure: Towards the essential graph by integer linear programming tools
International Journal of Approximate Reasoning
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This paper addresses exact learning of Bayesian network structure from data and expert's knowledge based on score functions that are decomposable. First, it describes useful properties that strongly reduce the time and memory costs of many known methods such as hill-climbing, dynamic programming and sampling variable orderings. Secondly, a branch and bound algorithm is presented that integrates parameter and structural constraints with data in a way to guarantee global optimality with respect to the score function. It is an any-time procedure because, if stopped, it provides the best current solution and an estimation about how far it is from the global solution. We show empirically the advantages of the properties and the constraints, and the applicability of the algorithm to large data sets (up to one hundred variables) that cannot be handled by other current methods (limited to around 30 variables).