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  Constraints as Data: a New Perspective on Inferring Probabilities

Jaeger, M. (2001). Constraints as Data: a New Perspective on Inferring Probabilities. In B. Nebel (Ed.), Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI-01) (pp. 755-760). San Francisco, USA: Morgan Kaufmann.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-000F-3206-5 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-000F-3207-3
Genre: Conference Paper

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ijcai01final.pdf (Any fulltext), 109KB
 
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 Creators:
Jaeger, Manfred1, Author           
Affiliations:
1Programming Logics, MPI for Informatics, Max Planck Society, ou_40045              

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 Abstract: We present a new approach to inferring a probability distribution which is incompletely specified by a number of linear constraints. We argue that the currently most popular approach of entropy maximization depends on a ``constraints as knowledge'' interpretation of the constraints, and that a different ``constraints as data'' perspective leads to a completely different type of inference procedures by statistical methods. With statistical methods some of the counterintuitive results of entropy maximization can be avoided, and inconsistent sets of constraints can be handled just like consistent ones. A particular statistical inference method is developed and shown to have a nice robustness property.

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Language(s): eng - English
 Dates: 2010-03-122001
 Publication Status: Issued
 Pages: -
 Publishing info: San Francisco, USA : Morgan Kaufmann
 Table of Contents: -
 Rev. Type: -
 Identifiers: eDoc: 519817
URI: http://www.mpi-sb.mpg.de/~jaeger/publications/IJCAI01.ps.gz
Other: Local-ID: C1256104005ECAFC-BFFAFD324692662CC1256AA90049F315-JaegerIJCAI01
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Title: Untitled Event
Place of Event: Seattle, USA
Start-/End Date: 2001

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Title: Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI-01)
Source Genre: Proceedings
 Creator(s):
Nebel, Bernhard, Editor
Affiliations:
-
Publ. Info: San Francisco, USA : Morgan Kaufmann
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 755 - 760 Identifier: ISBN: 1-55860-777-3