Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Learning hybrid Bayesian networks from data
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
Causal Probabilistic Networks with Both Discrete and Continuous Variables
IEEE Transactions on Pattern Analysis and Machine Intelligence
On convergence properties of the em algorithm for gaussian mixtures
Neural Computation
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Learning Bayesian networks for discrete data
Computational Statistics & Data Analysis
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In this paper, a new method for learning Bayesian networks structure with continuous variables is proposed. The continuous variables are discretized based on hybrid data clustering. The discrete values of a continuous variable are obtained by using father node structure and Gibbs sampling. Optimal dimension of discretized continuous variable is found by MDL principle to the Markov blanket. Dependent relationship is refined by optimization regulation to Bayesian network structure in iteration learning.