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Conference Paper

Relational Bayesian Networks

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

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Citation

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


Cite as: https://hdl.handle.net/11858/00-001M-0000-000F-3A08-5
Abstract
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.