Sensitivity Analysis for Threshold Decision Making with Dynamic Networks
Abstract
The effect of inaccuracies in the parameters of a dynamic Bayesian network can be investigated by subjecting the network to a sensitivity analysis. Having detailed the resulting sensitivity functions in our previous work, we now study the effect of parameter inaccuracies on a recommended decision in view of a threshold decision-making model. We detail the effect of varying a single and multiple parameters from a conditional probability table and present a computational procedure for establishing bounds between which assessments for these parameters can be varied without inducing a change in the recommended decision. We illustrate the various concepts involved by means of a real-life dynamic network in the field of infectious disease.
Cite
@article{arxiv.1206.6818,
title = {Sensitivity Analysis for Threshold Decision Making with Dynamic Networks},
author = {Theodore Charitos and Linda C. van der Gaag},
journal= {arXiv preprint arXiv:1206.6818},
year = {2012}
}
Comments
Appears in Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence (UAI2006)