Uncertainty in Artificial Intelligence
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Elicitation of Probabilities for Belief Networks: Combining Qualitative and Quantitative Information
Marek Druzdzel, Linda van der Gaag
Abstract:
Although the usefulness of belief networks for reasoning under uncertainty is widely accepted, obtaining numerical probabilities that they require is still perceived a major obstacle. Often not enough statistical data is available to allow for reliable probability estimation. Available information may not be directly amenable for encoding in the network. Finally, domain experts may be reluctant to provide numerical probabilities. In this paper, we propose a method for elicitation of probabilities from a domain expert that is non-invasive and accommodates whatever probabilistic information the expert is willing to state. We express all available information, whether qualitative or quantitative in nature, in a canonical form consisting of (in) equalities expressing constraints on the hyperspace of possible joint probability distributions. We then use this canonical form to derive second-order probability distributions over the desired probabilities.
Keywords: Bayesian belief networks, knowledge acquisition,elicitation of probabilities.
Pages: 141-148
PS Link: http://www.pitt.edu/~druzdzel/psfiles/uai95.ps
PDF Link: /papers/95/p141-druzdzel.pdf
BibTex:
@INPROCEEDINGS{Druzdzel95,
AUTHOR = "Marek Druzdzel and Linda van der Gaag",
TITLE = "Elicitation of Probabilities for Belief Networks: Combining Qualitative and Quantitative Information",
BOOKTITLE = "Proceedings of the Eleventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-95)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1995",
PAGES = "141--148"
}


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