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
