Uncertainty in Artificial Intelligence
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Measure Selection: Notions of Rationality and Representation Independence
Manfred Jaeger
Abstract:
We take another look at the general problem of selecting a preferred probability measure among those that comply with some given constraints. The dominant role that entropy maximization has obtained in this context is questioned by arguing that the minimum information principle on which it is based could be supplanted by an at least as plausible "likelihood of evidence" principle. We then review a method for turning given selection functions into representation independent variants, and discuss the tradeoffs involved in this transformation.
Keywords:
Pages: 274-281
PS Link:
PDF Link: /papers/98/p274-jaeger.pdf
BibTex:
@INPROCEEDINGS{Jaeger98,
AUTHOR = "Manfred Jaeger ",
TITLE = "Measure Selection: Notions of Rationality and Representation Independence",
BOOKTITLE = "Proceedings of the Fourteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-98)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1998",
PAGES = "274--281"
}


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