Prior knowledge elicitation: The past, present, and future

Petrus Mikkola, Osvaldo Martin, Suyog Halasinamara Chandramouli, Marcelo Hartmann, Oriol Abril Pla, Owen Thomas, Henri Pesonen, Jukka Corander, Aki Vehtari, Samuel Kaski, Paul-Christian Burkner, Arto Klami*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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
Number of pages60
JournalSubmitted
Publication statusSubmitted - 30 Nov 2021
MoE publication typeA1 Journal article-refereed

Keywords

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

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