  The prior distribution for the unknown model parameters plays a crucial role
in the process of statistical inference based on Bayesian methods. However,
specifying suitable priors is often difficult even when detailed prior
knowledge is available in principle. The challenge is to express quantitative
information in the form of a probability distribution. Prior elicitation
addresses this question by extracting subjective information from an expert and
transforming it into a valid prior. Most existing methods, however, require
information to be provided on the unobservable parameters, whose effect on the
data generating process is often complicated and hard to understand. We propose
an alternative approach that only requires knowledge about the observable
outcomes - knowledge which is often much easier for experts to provide.
Building upon a principled statistical framework, our approach utilizes the
prior predictive distribution implied by the model to automatically transform
experts judgements about plausible outcome values to suitable priors on the
parameters. We also provide computational strategies to perform inference and
guidelines to facilitate practical use.
