Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Background and Perspectives of Possibilistic Graphical Models
ECSQARU/FAPR '97 Proceedings of the First International Joint Conference on Qualitative and Quantitative Practical Reasoning
Anytime Possibilistic Propagation Algorithm
Soft-Ware 2002 Proceedings of the First International Conference on Computing in an Imperfect World
Possibilistic network-based classifiers: on the reject option and concept drift issues
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
Belief revision of product-based causal possibilistic networks
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Inference in possibilistic network classifiers under uncertain observations
Annals of Mathematics and Artificial Intelligence
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In possibility theory, there are two kinds of possibilistic causal networks depending if the possibilistic conditioning is based on the minimum or the product operator. Product-based possibilistic networks share the same practical and theoretical features as Bayesian networks. In this paper, we focus on min-based causal networks and propose a propagation algorithm for such networks. The basic idea is first to transform the initial network only into a moral graph. Then, two different procedures, called stabilization and checking consistency, are applied to compute the possibility degree of any variable of interest given some evidence.