  Specification of the prior distribution for a Bayesian model is a central
part of the Bayesian workflow for data analysis, but it is often difficult even
for statistical experts. In principle, prior elicitation transforms domain
knowledge of various kinds into well-defined prior distributions, and offers a
solution to the prior specification problem. In practice, however, we are still
fairly far from having usable prior elicitation tools that could significantly
influence the way we build probabilistic models in academia and industry. We
lack elicitation methods that integrate well into the Bayesian workflow and
perform elicitation efficiently in terms of costs of time and effort. We even
lack a comprehensive theoretical framework for understanding different facets
of the prior elicitation problem.
  Why are we not widely using prior elicitation? We analyse the state of the
art by identifying a range of key aspects of prior knowledge elicitation, from
properties of the modelling task and the nature of the priors to the form of
interaction with the expert. The existing prior elicitation literature is
reviewed and categorized in these terms. This allows recognizing under-studied
directions in prior elicitation research, finally leading to a proposal of
several new avenues to improve prior elicitation methodology.
