On the uniqueness of possibilistic measure of uncertainty and information
Fuzzy Sets and Systems - Special Issue: Measures of Uncertainty
Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Uncertainty and vagueness in knowledge based systems
Uncertainty and vagueness in knowledge based systems
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
C4.5: programs for machine learning
C4.5: programs for machine learning
Learning in graphical models
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Graphical Models: Methods for Data Analysis and Mining
Graphical Models: Methods for Data Analysis and Mining
Foundations of Fuzzy Systems
Causal networks: semantics and expressiveness
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
On biases in estimating multi-valued attributes
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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
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One focus of research in graphical models is how to learn them from a dataset of sample cases. This learning task can pose unpleasant problems if the dataset to learn from contains imprecise information in the form of sets of alternatives instead of precise values. 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. Since the search methods employed in a learning algorithm are relatively independent of the underlying uncertainty or imprecision calculus, we focus on evaluation measures (or scoring functions).