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Conference Paper

Convergence Results for Relational Bayesian Networks

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

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Citation

Jaeger, M. (1998). Convergence Results for Relational Bayesian Networks. In V. Pratt (Ed.), Proceedings of the 13th Annual IEEE Symposium on Logic in Computer Science (LICS-98) (pp. 44-55). Los Alamitos, USA: IEEE.


Cite as: https://hdl.handle.net/11858/00-001M-0000-000F-3828-D
Abstract
Relational Bayesian networks are an extension of the method of probabilistic model construction by Bayesian networks. They define probability distributions on finite relational structures by conditioning the probability of a ground atom $r(a_1,\ldots,a_n)$\ on first-order properties of $a_1,\ldots,a_n$\ that have been established by previous random decisions. In this paper we investigate from a finite model theory perspective the convergence properties of the distributions defined in this manner. A subclass of relational Bayesian networks is identified that define distributions with convergence laws for first-order properties.