Most research on rule-based inference under uncertainty has
focused on the normative validity and efficiency of various
belief-update algorithms. In this paper we shift the attention
to the inputs of these algorithms, namely, to the degrees of
beliefs elicited from domain experts. Classical methods for
eliciting continuous probability functions are of little use in a
rule-based model, where propositions of interest are taken to be
causally related and, typically, discrete, random variables. We
take the position that the numerical encoding of degrees of
belief in such propositions is somewhat analogous to the
measurement of physical stimuli like brightness, weight, and
distance. With that in mind, we base our elicitation techniques
on statements regarding the relative likelihoods of various clues
and hypotheses. We propose a formal procedure designed to (a)
elicit such inputs in a credible manner, and, (b) transform them
into the conditional probabilities and likelihood-ratios required
by Bayesian inference systems.Information Systems Working Papers Serie