Methods for Eliciting Informative Prior Distributions: A Critical Review

Julia R. Falconer (), Eibe Frank (), Devon L. L. Polaschek () and Chaitanya Joshi ()
Additional contact information
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

Decision Analysis, 2022, vol. 19, issue 3, 189-204

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.

Keywords: Bayesian inference; informative priors; prior distribution; prior elicitation; uncertainty (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://dx.doi.org/10.1287/deca.2022.0451 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ordeca:v:19:y:2022:i:3:p:189-204

Access Statistics for this article

More articles in Decision Analysis from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().