Uncertainty in Artificial Intelligence
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Making Sensitivity Analysis Computationally Efficient
Uffe Kjærulff, Linda van der Gaag
Abstract:
To investigate the robustness of the output probabilities of a Bayesian network, a sensitivity analysis can be performed. A one-way sensitivity analysis establishes, for each of the probability parameters of a network, a function expressing a posterior marginal probability of interest in terms of the parameter. Current methods for computing the coefficients in such a function rely on a large number of network evaluations. In this paper, we present a method that requires just a single outward propagation in a junction tree for establishing the coefficients in the functions for all possible parameters; in addition, an inward propagation is required for processing evidence. Conversely, the method requires a single outward propagation for computing the coefficients in the functions expressing all possible posterior marginals in terms of a single parameter. We extend these results to an n-way sensitivity analysis in which sets of parameters are studied.
Keywords: Sensitivity analysis, Bayesian network, junction tree
Pages: 317-325
PS Link: http://www.cs.auc.dk/~uk/papers/kjaerulff-gaag-00.ps.gz
PDF Link: /papers/00/p317-kjaerulff.pdf
BibTex:
@INPROCEEDINGS{Kjærulff00,
AUTHOR = "Uffe Kjærulff and Linda van der Gaag",
TITLE = "Making Sensitivity Analysis Computationally Efficient",
BOOKTITLE = "Proceedings of the Sixteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-00)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2000",
PAGES = "317--325"
}


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