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Methods for Eliciting Informative Prior Distributions: A Critical Review

Author

Listed:
  • Julia R. Falconer

    (Department of Mathematics, University of Waikato, Hamilton 3216, New Zealand; New Zealand Institute of Security and Crime Science, University of Waikato, Hamilton 3216, New Zealand)

  • Eibe Frank

    (Department of Computer Science, University of Waikato, Hamilton 3216, New Zealand)

  • Devon L. L. Polaschek

    (New Zealand Institute of Security and Crime Science, University of Waikato, Hamilton 3216, New Zealand; School of Psychology, University of Waikato, Hamilton 3216, New Zealand)

  • Chaitanya Joshi

    (Department of Mathematics, University of Waikato, Hamilton 3216, New Zealand; New Zealand Institute of Security and Crime Science, University of Waikato, Hamilton 3216, New Zealand)

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.

Suggested Citation

  • Julia R. Falconer & Eibe Frank & Devon L. L. Polaschek & Chaitanya Joshi, 2022. "Methods for Eliciting Informative Prior Distributions: A Critical Review," Decision Analysis, INFORMS, vol. 19(3), pages 189-204, September.
  • Handle: RePEc:inm:ordeca:v:19:y:2022:i:3:p:189-204
    DOI: 10.1287/deca.2022.0451
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    References listed on IDEAS

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    Cited by:

    1. Peng Yang & Yuansong Zhao & Lei Nie & Jonathon Vallejo & Ying Yuan, 2023. "SAM: Self‐adapting mixture prior to dynamically borrow information from historical data in clinical trials," Biometrics, The International Biometric Society, vol. 79(4), pages 2857-2868, December.
    2. Julia R. Falconer & Eibe Frank & Devon L. L. Polaschek & Chaitanya Joshi, 2024. "Eliciting Informative Priors by Modeling Expert Decision Making," Decision Analysis, INFORMS, vol. 21(2), pages 77-90, June.

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