Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Optimal structure identification with greedy search
The Journal of Machine Learning Research
Finding a path is harder than finding a tree
Journal of Artificial Intelligence Research
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A transformational characterization of equivalent Bayesian network structures
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Inference of structures of models of probabilistic dependences from statistical data
Cybernetics and Systems Analysis
Learning Factor Graphs in Polynomial Time and Sample Complexity
The Journal of Machine Learning Research
Minimal separators in dependency structures: Properties and identification
Cybernetics and Systems Analysis
Structure learning of Bayesian networks using constraints
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A comparison of novel and state-of-the-art polynomial Bayesian network learning algorithms
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Globally optimal structure learning of Bayesian networks from data
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Constructing the Bayesian network structure from dependencies implied in multiple relational schemas
Expert Systems with Applications: An International Journal
Efficient Structure Learning of Bayesian Networks using Constraints
The Journal of Machine Learning Research
Markov blankets and meta-heuristics search: sentiment extraction from unstructured texts
WebKDD'04 Proceedings of the 6th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
An optimization-based approach for the design of Bayesian networks
Mathematical and Computer Modelling: An International Journal
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In this paper, we provide new complexity results for algorithms that learn discretevariable Bayesian networks from data. Our results apply whenever the learning algorithm uses a scoring criterion that favors the simplest model able to represent the generative distribution exactly. Our results therefore hold whenever the learning algorithm uses a consistent scoring criterion and is applied to a sufficiently large dataset. We show that identifying high-scoring structures is NP-hard, even when we are given an independence oracle, an inference oracle, and/or an information oracle. Our negative results also apply when learning discrete-variable Bayesian networks in which each node has at most k parents, for all k ≥ 3.