Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
PULCinella: a general tool for propagating uncertainty in valuation networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Probabilistic similarity networks
Probabilistic similarity networks
Valuation-based systems: a framework for managing uncertainty in expert systems
Fuzzy logic for the management of uncertainty
Structure identification in relational data
Artificial Intelligence - Special volume on constraint-based reasoning
Learning in graphical models
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Foundations of Fuzzy Systems
Causal networks: semantics and expressiveness
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
The Bayesian structural EM algorithm
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Update rules for parameter estimation in Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
An Efficient Algorithm for Naive Possibilistic Classifiers with Uncertain Inputs
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
Possibility theory and statistical reasoning
Computational Statistics & Data Analysis
Jeffrey's rule of conditioning in a possibilistic framework
Annals of Mathematics and Artificial Intelligence
Inference in possibilistic network classifiers under uncertain observations
Annals of Mathematics and Artificial Intelligence
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Graphical models -- especially probabilistic networks like Bayes networks and Markov networks -- are very popular to make reasoning in high-dimensional domains feasible. Since constructing them manually can be tedious and time consuming, a large part of recent research has been devoted to learning them from data. However, if the dataset to learn from contains imprecise information in the form of sets of alternatives instead of precise values, this learning task can pose unpleasant problems. In this paper we study an approach to cope with these problems, which is not based on probability theory as the more common approaches like, e.g., expectation maximization, but uses possibility theory as the underlying calculus of a graphical model.