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Constraints as Data: a New Perspective on Inferring Probabilities

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

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Zitation

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.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-000F-3206-5
Zusammenfassung
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.