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
Text
Charitos and van der Gaag. "Sensitivity Analysis for Threshold Decision Making with Dynamic Networks." Conference on Uncertainty in Artificial Intelligence, 2006.Markdown
[Charitos and van der Gaag. "Sensitivity Analysis for Threshold Decision Making with Dynamic Networks." Conference on Uncertainty in Artificial Intelligence, 2006.](https://mlanthology.org/uai/2006/charitos2006uai-sensitivity/)BibTeX
@inproceedings{charitos2006uai-sensitivity,
title = {{Sensitivity Analysis for Threshold Decision Making with Dynamic Networks}},
author = {Charitos, Theodore and van der Gaag, Linda C.},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2006},
url = {https://mlanthology.org/uai/2006/charitos2006uai-sensitivity/}
}