Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Uncertain Information Processing in Expert Systems
Uncertain Information Processing in Expert Systems
Decision Making and Medical Care: Proceedings
Decision Making and Medical Care: Proceedings
Separoids: A Mathematical Framework for Conditional Independence and Irrelevance
Annals of Mathematics and Artificial Intelligence
Stochastic Independence in a Coherent Setting
Annals of Mathematics and Artificial Intelligence
Probabilistic Reasoning as a General Unifying Tool
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Stochastic independence for upper and lower probabilities in a coherent setting
Technologies for constructing intelligent systems
Conditional independence structures and graphical models
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Graphoid properties of qualitative possibilistic independence relations
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Conditional independence structure and its closure: Inferential rules and algorithms
International Journal of Approximate Reasoning
Acyclic Directed Graphs to Represent Conditional Independence Models
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Possibility theory: Conditional independence
Fuzzy Sets and Systems
Acyclic directed graphs representing independence models
International Journal of Approximate Reasoning
Exploiting independencies to compute semigraphoid and graphoid structures
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
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A definition of stochastic independence which avoids the inconsistencies (related to events of probability 0 or 1) of the classic one has been proposed by Coletti and Scozzafava for two events. We extend it to iconditional independence among finite sets of events. In particular, the case of (finite) discrete random variables is studied. We check which of the relevant properties connected with graphical structures hold. Hence, an axiomatic characterization of these independence models is given and it is compared to the classic ones.