Reasoning About Infinite Random Structures with Relational Bayesian Networks

Jaeger, Manfred

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Reasoning About Infinite Random Structures with Relational Bayesian Networks

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

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Jaeger, M. (1998). Reasoning About Infinite Random Structures with Relational Bayesian Networks. In A. G. Cohn, L. Schubert, & S. C. Shapiro (Eds.), Proceedings of the 6th International Conference on Principles of Knowledge Representation and Reasoning (KR-98) (pp. 570-581). San Francisco, USA: Morgan Kaufmann.

Cite as: https://hdl.handle.net/11858/00-001M-0000-000F-3874-3

Abstract

Relational Bayesian networks extend standard Bayesian networks by integrating some of the expressive power of first-order logic into the Bayesian network paradigm. As in the case of the related technique of knowledge based model construction, so far, decidable semantics only have been provided for finite stochastic domains. In this paper we extend the semantics of relational Bayesian networks, so that they also define probability distributions over countably infinite structures. Using a technique remeniscent of quantifier elimination methods in model theory, we show that probabilistic queries about these distributions are decidable.