Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Learning belief networks from data: an information theory based approach
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
LAZY propagation: a junction tree inference algorithm based on lazy evaluation
Artificial Intelligence
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Learning Bayesian Networks from Incomplete Data Based on EMI Method
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
The Bayesian structural EM algorithm
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
An algorithm for finding minimum d-separating sets in belief networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Robust independence testing for constraint-based learning of causal structure
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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Generally speaking, dependency analysis based Bayesian network learning algorithms are of higher efficiency. J. Cheng’s algorithm is a representative of this kinds of algorithms, while its efficiency could be improved further. This paper presents an efficient Bayesian network learning algorithm, which is an improvement to J. Cheng’s algorithm that uses Mutual Information (MI) and Conditional Mutual Information (CMI) as Conditional Independence (CI) tests. Through redefining the equations for calculating MI and CMI, our algorithm could decrease a large number of basic operations such as logarithms, divisions etc. and reduce the times of access to datasets to the minimum. Moreover, to efficiently calculate CMI, an efficient method for finding an approximate minimum cut-set is proposed in our algorithm. Experimental results show that under the same accuracy, our algorithm is much more efficient than J. Cheng’s algorithm.