Uncertainty in Prior Elicitaions: A Non-Parametric Approach
CEAR Seminar Room – RCB 1112 – from 10:30am – 12:00pm
A key task in the elicitation of expert knowledge is to construct a distribution from the ﬁnite, and usually small, number of statements that have been elicited from the expert. These statements typically specify some quantiles or moments of the distribution. Such statements are not enough to identify the expert’s probability distribution uniquely, and the usual approach is to ﬁt some member of a convenient parametric family. There are two clear deﬁciencies in this solution. First, the expert’s beliefs are forced to ﬁt the parametric family. Secondly, no account is then taken of the many other possible distributions that might have ﬁtted the elicited statements equally well. We present a nonparametric approach which tackles both of these deﬁciencies. We also consider the issue of the imprecision in the elicited probability judgements.