Uncertainty in Artificial Intelligence
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Evidence-invariant Sensitivity Bounds
Silja Renooij, Linda van der Gaag
Abstract:
The sensitivities revealed by a sensitivity analysis of a probabilistic network typically depend on the entered evidence. For a real-life network therefore, the analysis is performed a number of times, with different evidence. Although efficient algorithms for sensitivity analysis exist, a complete analysis is often infeasible because of the large range of possible combinations of observations. In this paper we present a method for studying sensitivities that are invariant to the evidence entered. Our method builds upon the idea of establishing bounds between which a parameter can be varied without ever inducing a change in the most likely value of a variable of interest.
Keywords: null
Pages: 479-486
PS Link:
PDF Link: /papers/04/p479-renooij.pdf
BibTex:
@INPROCEEDINGS{Renooij04,
AUTHOR = "Silja Renooij and Linda van der Gaag",
TITLE = "Evidence-invariant Sensitivity Bounds",
BOOKTITLE = "Proceedings of the Twentieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-04)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2004",
PAGES = "479--486"
}


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