Uncertainty in Artificial Intelligence
First Name   Last Name   Password   Forgot Password   Log in!
    Proceedings         Authors   Author's Info   Article details         Search    
Making Sensitivity Analysis Computationally Efficient
Uffe Kjærulff, Linda van der Gaag
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
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"

hosted by DSL   •   site info   •   help