Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Adaptive Probabilistic Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
A view of the EM algorithm that justifies incremental, sparse, and other variants
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Estimating dependency structure as a hidden variable
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Learning Belief Networks in the Presence of Missing Values and Hidden Variables
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Optimal structure identification with greedy search
The Journal of Machine Learning Research
Learning Bayesian networks from incomplete data
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Learning Causal Bayesian Networks from Incomplete Observational Data and Interventions
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
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This paper proposes a new method, named Greedy Equivalence Search-Expectation Maximization (GES-EM), for learning Bayesian networks from incomplete data. Our method extends the recently proposed GES algorithm to deal with incomplete data. Evaluation of generated networks was done using expected Bayesian Information Criterion (BIC) scoring function. Experimental results show that GES-EM algorithm yields more accurate structures than the standard Alternating Model Selection-Expectation Maximization (AMS-EM) algorithm.