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Conference Paper

Relational Bayesian Networks


Jaeger,  Manfred
Programming Logics, MPI for Informatics, Max Planck Society;

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Jaeger, M. (1997). Relational Bayesian Networks. In D. Geiger, & P. P. Shenoy (Eds.), Proceedings of the 13th Conference of Uncertainty in Artificial Intelligence (pp. 266-273). San Francisco, USA: Morgan Kaufmann.

Cite as: https://hdl.handle.net/11858/00-001M-0000-000F-3A08-5
A new method is developed to represent probabilistic relations on
multiple random events. Where previously knowledge bases containing
probabilistic rules were used for this purpose, here a probability
distribution over the relations is directly represented by a
Bayesian network. By using a powerful way of specifying conditional probability
distributions in these networks, the resulting formalism is more
expressive than the previous ones. Particularly, it provides for constraints
on equalities of events, and it allows to define complex, nested
combination functions.