Tony O'Hagan - Academic pages - Abstracts

## Uncertainty in Prior Elicitations: a
Nonparametric Approach.

Jeremy Oakley and Anthony O'Hagan

*Department of Probability and Statistics, University of Sheffield,
Sheffield, England*

**Publication details: **
*Biometrika* **94**, 427-441, 2007

### Abstract

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.

**KEY WORDS:** Expert elicitation, Gaussian process,
non-parametric density estimation.

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Updated: 31 July 2007
Maintained by: Tony O'Hagan