Methods for Eliciting Informative Prior Distributions
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Eliciting informative prior distributions for Bayesian inference can often be complex and challenging. While popular methods rely on asking experts probability based questions to quantify uncertainty, these methods are not without their drawbacks and many alternative elicitation methods exist. This paper explores methods for eliciting informative priors categorized by type and briefly discusses their strengths and limitations. Two representative applications are used throughout to explore the suitability, or lack thereof, of the existing methods for eliciting informative priors for these problems. The primary aim of this work is to highlight some of the gaps in the present state of art and identify directions for future research.
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