On the Desirability of Acyclic Database Schemes
Journal of the ACM (JACM)
Strong Conditional Independence for Credal Sets
Annals of Mathematics and Artificial Intelligence
Formal Properties of Conditional Independence in Different Calculi of AI
ECSQARU '93 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
New Semantics for Quantitative Possibility Theory
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Computing lower and upper expectations under epistemic independence
International Journal of Approximate Reasoning
Inference in directed evidential networks based on the transferable belief model
International Journal of Approximate Reasoning
Conditional independence structure and its closure: Inferential rules and algorithms
International Journal of Approximate Reasoning
Combination of partially non-distinct beliefs: The cautious-adaptive rule
International Journal of Approximate Reasoning
Composition of probability measures on finite spaces
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Acyclic directed graphs representing independence models
International Journal of Approximate Reasoning
Local computations in Dempster--Shafer theory of evidence
International Journal of Approximate Reasoning
Probabilistic compositional models: Solution of an equivalence problem
International Journal of Approximate Reasoning
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The goal of the paper is twofold. The first is to show that some of the ideas for representation of multidimensional distributions in probability and possibility theories can be transferred into evidence theory. Namely, we show that multidimensional basic assignments can be rather efficiently represented in a form of so-called compositional models. These models are based on the iterative application of the operator of composition, whose definition for basic assignments as well as its properties are presented. We also prove that the operator of composition in evidence theory is in a sense generalization of its probabilistic counterpart. The second goal of the paper is to introduce a new definition of conditional independence in evidence theory and to show in what sense it is superior to that formerly introduced by other authors.