2013/03/06 by Prakash P. Shenoy, Shenoy, Prakash P.
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Logic, Reasoning, and Knowledge #Multi-Criteria Decision Making #cs.AI
paper · pdf · doi:10.48550/arxiv.1303.1477
Appears in Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence (UAI1993)
arxiv created 2013/03/06 · openalex publication_date 2013/03/06 · arxiv updated 2013/03/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.