Methods for Representing Bias in Bayesian Networks
Abstract
Bias is intrinsic to observation and reasoning in both humans and automated systems. Bayesian Belief Networks (BBNs) are well suited for representing these biases and for applying bias models to improve reasoning practices, but there are a number of different ways that bias can be represented and integrated into reasoning processes using BBNs. In this paper, we describe a number of methods to model biases using BBNs and discuss the strengths and weaknesses of each method. 1.
Cite
Text
Carlson et al. "Methods for Representing Bias in Bayesian Networks." Conference on Uncertainty in Artificial Intelligence, 2008.Markdown
[Carlson et al. "Methods for Representing Bias in Bayesian Networks." Conference on Uncertainty in Artificial Intelligence, 2008.](https://mlanthology.org/uai/2008/carlson2008uai-methods/)BibTeX
@inproceedings{carlson2008uai-methods,
title = {{Methods for Representing Bias in Bayesian Networks}},
author = {Carlson, Eric and Guarino, Sean L. and Pfautz, Jonathan D.},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2008},
url = {https://mlanthology.org/uai/2008/carlson2008uai-methods/}
}