Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
A Bayesian Multiresolution Independence Test for Continuous Variables
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
A Linear Non-Gaussian Acyclic Model for Causal Discovery
The Journal of Machine Learning Research
Distribution-free learning of Bayesian network structure in continuous domains
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
A partial correlation-based algorithm for causal structure discovery with continuous variables
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
An efficient causal discovery algorithm for linear models
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning bayesian networks from Markov random fields: An efficient algorithm for linear models
ACM Transactions on Knowledge Discovery from Data (TKDD)
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In this paper, we propose a heuristic partial-correlation-based (HP) algorithm to discover causal structures of Bayesian networks with continuous variables. There are two advantages of HP algorithm compared with existing ones: the first is that HP algorithm has a polynomial time complexity in the worst case, and the second HP algorithm can be applied to the data generated by linear simultaneous equation model, without assuming data following multivariate Gaussian distribution. Empirical results show that HP algorithm outperforms existing algorithms in both accuracy and run time.