for the population, and we write $$X_1, X_2,...\mid \mu, \sigma^2 \overset{\emph{iid}}{\thicksim} \mbox{N} (\mu, \sigma^2),$$ so that the aim is to obtain the expert’s probability distribution for $\mu$ and $\sigma^2$.

An obvious application would be in Bayesian inference for the parameters of a normal distribution, though in many cases the available data would dominate any reasonable prior, and the effort in obtaining expert prior knowledge may not be worthwhile. There are, however, various scenarios when little or even no data would be available, and the prior distribution plays an important/essential role. We defer discussion of these to the next section.

Typically, elicitation methods involving asking experts to make judgements about observable quantities, rather than parameters in statistical models. However, we believe experts would be willing to make judgements directly about an uncertain measure of location such as a mean or median, and generic techniques for eliciting beliefs about a scalar quantity would normally suffice, for example, the bisection method described in [@Raiffa:1968], pp161-168. See also [@Hagan:2006], Chapter 6. We would not expect experts to be willing to make direct judgements about an uncertain population variance, as this is a more abstract quantity. The challenge is then to design elicitation questions about 