A Bayesian Perspective on Confidence
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.
Cite
Text
Heckerman and Jimison. "A Bayesian Perspective on Confidence." Conference on Uncertainty in Artificial Intelligence, 1987.Markdown
[Heckerman and Jimison. "A Bayesian Perspective on Confidence." Conference on Uncertainty in Artificial Intelligence, 1987.](https://mlanthology.org/uai/1987/heckerman1987uai-bayesian/)BibTeX
@inproceedings{heckerman1987uai-bayesian,
title = {{A Bayesian Perspective on Confidence}},
author = {Heckerman, David and Jimison, Holly Brügge},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1987},
pages = {149-160},
url = {https://mlanthology.org/uai/1987/heckerman1987uai-bayesian/}
}