mlsanityData to 14 Oct 2026

Prior knowledge elicitation: The past, present, and future

arXiv 2112.01380 ↗First posted 1 Dec 2021stat.MEcited by 23

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Abstract (arXiv)

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. In principle, prior elicitation transforms domain knowledge of various kinds into well-defined prior distributions, and offers a solution to the prior specification problem. 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 analyse 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.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Dec 2021.

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Citing papers per year: 2022: 2; 2023: 6; 2024: 4; 2025: 5; 2026: 6.

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  1. 2023A Primer on Bayesian Neural Networks: Review and Debatescited by 27
  2. 2023Looping in the Human Collaborative and Explainable Bayesian Optimizationcited by 27
  3. 2024Principled Bayesian Optimisation in Collaboration with Human Expertscited by 5
  4. 2022On the safe use of prior densities for Bayesian model selectioncited by 4
  5. 2022Quantile-constrained Wasserstein projections for robust interpretability of numerical and machine learning modelscited by 4
  6. 2024Had enough of experts? Quantitative knowledge retrieval from large language modelscited by 4
  7. 2023Designing Optimal Behavioral Experiments Using Machine Learningcited by 3
  8. 2025Incorporating Expert Knowledge into Bayesian Causal Discovery of Mixtures of Directed Acyclic Graphscited by 2
  9. 2023Simulation-Based Prior Knowledge Elicitation for Parametric Bayesian Modelscited by 1
  10. 2024Preferential Normalizing Flowscited by 1
  11. 2024Expert-elicitation method for non-parametric joint priors using normalizing flowscited by 1
  12. 2025Normalizing Flow Regression for Bayesian Inference with Offline Likelihood Evaluationscited by 1
11 more
  1. 2026Multi-Agent Influence Diagrams to Hybrid Threat Modelingcited by 1
  2. 2026Human-in-the-Loop Meta Bayesian Optimization for Fusion Energy and Scientific Applicationscited by 1
  3. 2026Decision-Aligned Evaluation of Uncertainty Quantificationcited by 1
  4. 2026Amortized Bayesian Inference on Multilevel Models of Arbitrary Structurecited by 1
  5. 2023Incorporating Expert Opinion on Observable Quantities into Statistical Models -- A General Frameworkcited by 0
  6. 2023A tutorial on the Bayesian statistical approach to inverse problemscited by 0
  7. 2025Graphical Models for Decision-Making: Integrating Causality and Game Theorycited by 0
  8. 2025Using Large Language Models to Suggest Informative Prior Distributions in Bayesian Statisticscited by 0
  9. 2025Score-Based Density Estimation from Pairwise Comparisonscited by 0
  10. 2026Large language models as synthetic clinical experts to inform longitudinal rare-disease modelingcited by 0
  11. 2026Generative Priors Conditioned on Natural Language for Bayesian Inversion in PDEscited by 0