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  Relational Bayesian Networks

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

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-000F-3A08-5 Version Permalink: https://hdl.handle.net/21.11116/0000-000E-4366-4
Genre: Conference Paper

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

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 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.

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Language(s): eng - English
 Dates: 2010-03-121997
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: eDoc: 519506
Other: Local-ID: C1256104005ECAFC-41A7C85CEC93AC25C12564810014BF45-Jaeger97UAI
DOI: 10.48550/arXiv.1302.1550
BibTex Citekey: Jaeger_UAI97
 Degree: -

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Title: 13th Conference of Uncertainty in Artificial Intelligence
Place of Event: Providence, RI, USA
Start-/End Date: 1997-08-01 - 1997-08-03

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Title: Proceedings of the 13th Conference of Uncertainty in Artificial Intelligence
  Abbreviation : UAI 1997
Source Genre: Proceedings
 Creator(s):
Geiger, Dan1, Editor
Shenoy, Prakash Pundalik1, Editor
Affiliations:
1 External Organizations, ou_persistent22            
Publ. Info: San Francisco, USA : Morgan Kaufmann
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 266 - 273 Identifier: ISBN: 1-55860-485-5