Uncertainty in Artificial Intelligence
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Analysing Sensitivity Data from Probabilistic Networks
Linda van der Gaag, Silja Renooij
Abstract:
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.
Keywords:
Pages: 530-537
PS Link:
PDF Link: /papers/01/p530-van_der_gaag.pdf
BibTex:
@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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