Uncertainty in Artificial Intelligence
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Sensitivity Analysis for Probability Assessments in Bayesian Networks
Kathryn Laskey
Abstract:
When eliciting probability models from experts, knowledge engineers may compare the results of the model with expert judgment on test scenarios, then adjust model parameters to bring the behavior of the model more in line with the expert's intuition. This paper presents a methodology for analytic computation of sensitivity values to measure the impact of small changes in a network parameter on a target probability value or distribution. These values can be used to guide knowledge elicitation. They can also be used in a gradient descent algorithm to estimate parameter values that maximize a measure of goodness-of-fit to both local and holistic probability assessments.
Keywords:
Pages: 136-142
PS Link:
PDF Link: /papers/93/p136-laskey.pdf
BibTex:
@INPROCEEDINGS{Laskey93,
AUTHOR = "Kathryn Laskey ",
TITLE = "Sensitivity Analysis for Probability Assessments in Bayesian Networks",
BOOKTITLE = "Proceedings of the Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-93)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1993",
PAGES = "136--142"
}


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