# Methods for Eliciting Informative Prior Distributions: A Critical Review

arXiv: https://arxiv.org/abs/2112.07090 · HTML: https://mlsanity.com/paper/2112.07090/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2021.

## Built on by (most cited first, 3 of 3)

- [Had enough of experts? Quantitative knowledge retrieval from large language models](https://mlsanity.com/paper/2402.07770/) (2024)
- [Simulation-Based Prior Knowledge Elicitation for Parametric Bayesian Models](https://mlsanity.com/paper/2308.11672/) (2023)
- [Expert-elicitation method for non-parametric joint priors using normalizing flows](https://mlsanity.com/paper/2411.15826/) (2024)

## Abstract (arXiv)

Eliciting informative prior distributions for Bayesian inference can often be complex and challenging. While 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.