Statistical analysis with missing data
Statistical analysis with missing data
The EM algorithm for graphical association models with missing data
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
Learning from Incomplete Data
HUGIN: a shell for building Bayesian belief universes for expert systems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Approximation Methods for Efficient Learning of Bayesian Networks
Proceedings of the 2008 conference on Approximation Methods for Efficient Learning of Bayesian Networks
Trustworthy Service Selection and Composition
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
The Bayesian structural EM algorithm
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Learning mixtures of DAG models
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Learning bayesian network equivalence classes from incomplete data
DS'06 Proceedings of the 9th international conference on Discovery Science
Learning Bayesian network classifiers from label proportions
Pattern Recognition
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Much of the current research in learning Bayesian Networks fails to effectively deal with missing data. Most of the methods assume that the data is complete, or make the data complete using fairly ad-hoc methods; other methods do deal with missing data but learn only the conditional probabilities, assuming that the structure is known. We present a principled approach to learn both the Bayesian network structure as well as the conditional probabilities from incomplete data. The proposed algorithm is an iterative method that uses a combination of Expectation-Maximization (EM) and Imputation techniques. Results are presented on synthetic data sets which show that the performance of the new algorithm is much better than ad-hoc methods for handling missing data.