Uncertainty in Artificial Intelligence
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Analysing Sensitivity Data from Probabilistic Networks
Linda van der Gaag, Silja Renooij
With the advance of efficient analytical methods for sensitivity analysis ofprobabilistic networks, the interest in the sensitivities revealed by real-life networks is rekindled. As the amount of data resulting from a sensitivity analysis of even a moderately-sized network is alreadyoverwhelming, methods for extracting relevant information are called for. One such methodis to study the derivative of the sensitivity functions yielded for a network's parameters. We further propose to build upon the concept of admissible deviation, that is, the extent to which a parameter can deviate from the true value without inducing a change in the most likely outcome. We illustrate these concepts by means of a sensitivity analysis of a real-life probabilistic network in oncology.
Pages: 530-537
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PDF Link: /papers/01/p530-van_der_gaag.pdf
@INPROCEEDINGS{van der Gaag01,
AUTHOR = "Linda van der Gaag and Silja Renooij",
TITLE = "Analysing Sensitivity Data from Probabilistic Networks",
BOOKTITLE = "Proceedings of the Seventeenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-01)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2001",
PAGES = "530--537"

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