Uncertainty in Artificial Intelligence
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A Bayesian Perspective on Confidence
David Heckerman, Holly Jimison
Abstract:
We present a representation of partial confidence in belief and preference that is consistent with the tenets of decision-theory. The fundamental insight underlying the representation is that if a person is not completely confident in a probability or utility assessment, additional modeling of the assessment may improve decisions to which it is relevant. We show how a traditional decision-analytic approach can be used to balance the benefits of additional modeling with associated costs. The approach can be used during knowledge acquisition to focus the attention of a knowledge engineer or expert on parts of a decision model that deserve additional refinement.
Keywords: Bayesian, Confidence, Decision-Theory
Pages: 149-160
PS Link:
PDF Link: /papers/87/p149-heckerman.pdf
BibTex:
@INPROCEEDINGS{Heckerman87,
AUTHOR = "David Heckerman and Holly Jimison",
TITLE = "A Bayesian Perspective on Confidence",
BOOKTITLE = "Uncertainty in Artificial Intelligence 3 Annual Conference on Uncertainty in Artificial Intelligence (UAI-87)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1987",
PAGES = "149--160"
}


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