Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Gibbs sampling in Bayesian networks (research note)
Artificial Intelligence
The EM algorithm for graphical association models with missing data
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
A tutorial on learning with Bayesian networks
Learning in graphical models
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Learning Belief Networks in the Presence of Missing Values and Hidden Variables
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A hybrid approach for learning parameters of probabilistic networks from incomplete databases
Design and application of hybrid intelligent systems
Learning Bayesian Networks
Learning Bayesian networks from incomplete databases
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
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Existing methods of parameter and structure learning of Bayesian Networks (BNs) from a database assume that the database is complete. If there are missing values, they are assumed to be missing at random. This paper incorporates the concepts used in Dempster-Shafer theory of belief functions to learn both the parameters and structure of BNs. Instead of filling the missing values by their estimates, as it is done in the conventional techniques, the proposed approach models the missing values as representing ignorance or lack of belief of a system modeler in the actual state of the corresponding variables. The proposed representation modifies the existing algorithms for parameter and structure learning of BNs. The representation also allows a system modeler to add new findings in terms of support functions as used in belief functions theory; thus, providing a richer way to enter evidence in BNs.