Abstract

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. Prior elicitation transforms domain knowledge of various kinds into well-defined prior distributions, and offers a solution to the prior specification problem, in principle. 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 analyze 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.
Original languageEnglish
Article numberTBA
Pages (from-to)1-33
Number of pages33
JournalBayesian Analysis
VolumeTBA
Early online date4 May 2023
DOIs
Publication statusE-pub ahead of print - 4 May 2023
MoE publication typeA1 Journal article-refereed

Keywords

  • prior elicitation
  • prior distribution
  • informative prior
  • Bayesian workflow
  • domain knowledge

Fingerprint

Dive into the research topics of 'Prior knowledge elicitation: The past, present, and future'. Together they form a unique fingerprint.

Cite this