Help Privacy Policy Disclaimer
  Advanced SearchBrowse




Conference Paper

Constraints as Data: a New Perspective on Inferring Probabilities


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

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available

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.

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