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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 (UAI-13) (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/11858/00-001M-0000-000F-3A09-3
Genre: Conference Paper

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UAI97.ps.gz (Any fulltext), 44KB
 
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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: San Francisco, USA : Morgan Kaufmann
 Table of Contents: -
 Rev. Type: -
 Identifiers: eDoc: 519506
Other: Local-ID: C1256104005ECAFC-41A7C85CEC93AC25C12564810014BF45-Jaeger97UAI
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Title: Untitled Event
Place of Event: Providence, USA
Start-/End Date: 1997

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