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      Methods for eliciting informative prior distributions: A critical review

      Falconer, Julia R.; Frank, Eibe; Polaschek, Devon L. L.; Joshi, Chaitanya
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      Methods_for_Eliciting_Informative_Priors_-_FINAL.pdf
      Accepted version, 416.5Kb
      DOI
       10.1287/deca.2022.0451
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      Permanent link to Research Commons version
      https://hdl.handle.net/10289/14822
      Abstract
      Eliciting informative prior distributions for Bayesian inference can often be complex and challenging. Although 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. Most of the review literature in this field focuses on a particular type of elicitation approach. The primary aim of this work, however, is to provide a more complete yet macro view of the state of the art by highlighting new (and old) approaches in one clear easy-to-read article. Two representative applications are used throughout to explore the suitability, or lack thereof, of the existing methods, one of which highlights a challenge that has not been addressed in the literature yet. We identify some of the gaps in the present work and discuss directions for future research.
      Date
      2022
      Type
      Journal Article
      Publisher
      INFORMS
      Rights
      © 2022 INFORMS. This is the author's accepted version.
      Collections
      • Computing and Mathematical Sciences Papers [1452]
      • Arts and Social Sciences Papers [1403]
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