A Bayesian Perspective on Confidence
David Heckerman, Holly Jimison
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
PDF Link: /papers/87/p149-heckerman.pdf
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"