Relational Bayesian Networks
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.
Keywords: Bayesian networks, first-order logic.
PS Link: ftp://ftp.mpi-sb.mpg.de/pub/guide/staff/jaeger/publications/conferences/UAI97.ps.gz
PDF Link: /papers/97/p266-jaeger.pdf
AUTHOR = "Manfred Jaeger
TITLE = "Relational Bayesian Networks",
BOOKTITLE = "Proceedings of the Thirteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-97)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1997",
PAGES = "266--273"