Bibliographic Details
| Title: |
GLOBAL PROPAGATION IN BAYESIAN NETWORKS VS SEMIJOIN PROGRAMS IN RELATIONAL DATABASES. |
| Authors: |
WU, DAN, WONG, MICHAEL |
| Source: |
International Journal of Uncertainty, Fuzziness & Knowledge-Based Systems; Oct2005, Vol. 13 Issue 5, p539-560, 22p, 8 Diagrams, 4 Charts |
| Subject Terms: |
Uncertainty, Probability theory, Relational databases, Bayesian analysis |
| Abstract: |
Bayesian networks have been well established as an effective framework for uncertainty management using probability. Various methods for probabilistic reasoning in Bayesian networks have been developed and matured. Recently, research has shown that there exists an intriguing relationship between Bayesian networks and relational databases. Adding to that intriguing relationship, in this paper, we reveal that the global propagation method for probabilistic reasoning in Bayesian networks has a close tie with the well known semijoin programs for query answering in relational databases. This linkage between these two apparently different but closely related knowledge representations suggests that well developed techniques for query answering in relational databases could be applied to probabilistic reasoning in Bayesian networks for large and complex domains. [ABSTRACT FROM AUTHOR] |
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| Database: |
Complementary Index |