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  Reasoning About Infinite Random Structures with Relational Bayesian Networks

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.

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 Creators:
Jaeger, Manfred1, Author           
Affiliations:
1Programming Logics, MPI for Informatics, Max Planck Society, ou_40045              

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 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.

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Language(s): eng - English
 Dates: 2010-03-121998
 Publication Status: Issued
 Pages: -
 Publishing info: San Francisco, USA : Morgan Kaufmann
 Table of Contents: -
 Rev. Type: -
 Identifiers: eDoc: 519665
Other: Local-ID: C1256104005ECAFC-E176532FECBD651CC12566F0005BD749-Jaeger1998a
 Degree: -

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Title: Untitled Event
Place of Event: Trento, Italy
Start-/End Date: 1998

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Title: Proceedings of the 6th International Conference on Principles of Knowledge Representation and Reasoning (KR-98)
Source Genre: Proceedings
 Creator(s):
Cohn, Anthony G., Editor
Schubert, Lenhart, Editor
Shapiro, Stuart C., Editor
Affiliations:
-
Publ. Info: San Francisco, USA : Morgan Kaufmann
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 570 - 581 Identifier: ISBN: 1-55860-554-1