Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A valuation-based language for expert systems
International Journal of Approximate Reasoning
On Spohn's rule for revision of beliefs
International Journal of Approximate Reasoning
Valuation-based systems: a framework for managing uncertainty in expert systems
Fuzzy logic for the management of uncertainty
Conditional independence in uncertainty theories
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Using possibility theory in expert systems
Fuzzy Sets and Systems
Using Dempster-Shafer's belief-function theory in expert systems
Advances in the Dempster-Shafer theory of evidence
Multivalued dependencies and a new normal form for relational databases
ACM Transactions on Database Systems (TODS)
Probabilistic Expert Systems
Causal networks: semantics and expressiveness
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Representing belief function knowledge with graphical models
KSEM'11 Proceedings of the 5th international conference on Knowledge Science, Engineering and Management
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Valuation networks have been proposed as graphical representations of valuation-based systems (VBSs). The VBS framework is able to capture many uncertainty calculi including probability theory, Dempster-Shafer's belief-function theory, Spohn's epistemic belief theory, and Zadeh's possibility theory. In this paper, we show how valuation networks encode conditional independence relations. For the probabilistic case, the class of probability models encoded by valuation networks includes undirected graph models, directed acyclic graph models, directed balloon graph models, and recursive causal graph models.