Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning belief networks from data: an information theory based approach
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
A tutorial on learning with Bayesian networks
Learning in graphical models
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Causal discovery from a mixture of experimental and observational data
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Being Bayesian about network structure
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Intelligent and Robotic Systems
Strategies for improving the modeling and interpretability of Bayesian networks
Data & Knowledge Engineering
Algorithm for graphical Bayesian modeling based on multiple regressions
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Hierarchical multiple sensor fusion using structurally learned Bayesian network
WH '10 Wireless Health 2010
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Parallel Bayesian network structure learning with application to gene networks
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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Learning Bayesian network structure from large-scale datasets, without any expert-specified ordering of variables, remainsa difficult problem. We propose systematic improvements toautomatically learn Bayesian network structure from data. (1)We propose a linear parent search method to generate candidategraph. (2) We propose a comprehensive approach to eliminatecycles using minimal likelihood loss, a short cycle first heuristic,and a cut-edge repairing. (3) We propose structure perturbationto assess the stability of the network and a stability-improvementmethod to refine the network structure. The algorithms are easyto implement and efficient for large networks. Experimental resultson two data sets show that our new approach outperformsexisting methods.