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.
|Number of pages||5|
|State||Published - 1 Jan 2000|
|Event|| Proceedings of the Thirteenth International Florida Artificial Intelligence (FLAIRS) - Orlando, United States|
Duration: 22 May 2000 → 24 May 2000
|Conference||Proceedings of the Thirteenth International Florida Artificial Intelligence (FLAIRS)|
|Period||22/05/00 → 24/05/00|