Open Access
2023 Prior Knowledge Elicitation: The Past, Present, and Future
Petrus Mikkola, Osvaldo A. Martin, Suyog Chandramouli, Marcelo Hartmann, Oriol Abril Pla, Owen Thomas, Henri Pesonen, Jukka Corander, Aki Vehtari, Samuel Kaski, Paul-Christian Bürkner, Arto Klami
Author Affiliations +
Bayesian Anal. Advance Publication 1-33 (2023). DOI: 10.1214/23-BA1381


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.

Funding Statement

This work was supported by the Academy of Finland (Flagship program: Finnish Center for Artificial Intelligence FCAI), by the Technology Industries of Finland Centennial Foundation, by the Jane and Aatos Erkko Foundation, European Research Council grant 742158 (SCARABEE, Scalable inference algorithms for Bayesian evolutionary epidemiology), and by the UKRI Turing AI World-Leading Researcher Fellowship, EP/W002973/1. Partially funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2075 – 390740016.


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Petrus Mikkola. Osvaldo A. Martin. Suyog Chandramouli. Marcelo Hartmann. Oriol Abril Pla. Owen Thomas. Henri Pesonen. Jukka Corander. Aki Vehtari. Samuel Kaski. Paul-Christian Bürkner. Arto Klami. "Prior Knowledge Elicitation: The Past, Present, and Future." Bayesian Anal. Advance Publication 1 - 33, 2023.


Published: 2023
First available in Project Euclid: 4 May 2023

arXiv: 2112.01380
Digital Object Identifier: 10.1214/23-BA1381

Keywords: Bayesian workflow , domain knowledge , informative prior , prior distribution , prior elicitation

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