Independence Semantics for BKBs.

Eugene Santos, Tzachi Rosen, Eyal Shlomo Shimony

Research output: Contribution to conferencePaperpeer-review


Bayesian Knowledge Bases (BKB) are a rule-based probabilistic model that extend Bayes Networks (BN), by allowing context-sensitive independence and cycles in the directed graph. BKBs have probabilistic seman- tics, but lack independence semantics, i.e., a graph- based scheme determining what independence state- ments are sanctioned by the model. Such a semantics is provided through generalized d- separation, by constructing an equivalent BN. While useful for showing correctness, the construction is not practical for decision algorithms due to exponential size. Some results for special cases, where indepen- dence can be determined from polynomial-time tests on the BKB graph, are presented.
Original languageEnglish
Number of pages5
StatePublished - 1 Jan 2000
Event Proceedings of the Thirteenth International Florida Artificial Intelligence (FLAIRS) - Orlando, United States
Duration: 22 May 200024 May 2000


Conference Proceedings of the Thirteenth International Florida Artificial Intelligence (FLAIRS)
Country/TerritoryUnited States
Internet address


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