which we write as $$\sigma^2 \thicksim \mbox{IG} (a, b),$$ with density function, $$p(\sigma^2) = \frac{b^a}{\Gamma(a)} \sigma^{2^{-(a+1)}} \exp \left\{ \frac{-b}{\sigma^2} \right\}, \hspace{ 0.3 in} a , b > 0.$$ These are similar to the choices in [@KADANE:1980], except that their prior for $\mu$ was of the form $\mu | \sigma^2 \sim N(m, \sigma^2 v)$. In some cases, alternative families of distributions may be needed for $\mu$, and we discuss this further in Section \[sec:transform\]. The `SHELF` R package will fit either a gamma distribution or a lognormal distribution to the population precision $\sigma^{-2}$, though it would be difficult to claim any single choice of family as ‘optimal’ at this stage. After the expert has provided judgements and distributions have been fitted, we can use feedback to test whether these assumptions are acceptable to the expert; feedback is provided at several stages in our proposed method.

The proposed elicitation method {#sec:method}
===============================

For simplicity and ease of exposition, it is supposed that there is one female expert, and that the elicitation is conducted by a male facilitator. There are, of course, various general considerations when performing elicitation such as training of the experts, and how to manage (or combine opinions from) multiple 