Uncertainty in Artificial Intelligence
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Sensitivity Analysis for Threshold Decision Making with Dynamic Networks
Theodore Charitos, Linda van der Gaag
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.
Keywords:
Pages: 72-79
PS Link:
PDF Link: /papers/06/p72-charitos.pdf
BibTex:
@INPROCEEDINGS{Charitos06,
AUTHOR = "Theodore Charitos and Linda van der Gaag",
TITLE = "Sensitivity Analysis for Threshold Decision Making with Dynamic Networks",
BOOKTITLE = "Proceedings of the Twenty-Second Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-06)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2006",
PAGES = "72--79"
}


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