Prior knowledge elicitation: The past, present, and future
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2024-12
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Language
en
Pages
33
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Bayesian Analysis, Volume 19, issue 4, pp. 1129–1161
Abstract
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.Description
Keywords
Bayesian workflow, domain knowledge, informative prior, prior distribution, prior elicitation
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Citation
Mikkola, P, Martin, O, Halasinamara Chandramouli, S, Hartmann, M, Abril Pla, O, Thomas, O, Pesonen, H, Corander, J, Vehtari, A, Kaski, S, Burkner, P-C & Klami, A 2024, ' Prior knowledge elicitation: The past, present, and future ', Bayesian Analysis, vol. 19, no. 4, pp. 1129–1161 . https://doi.org/10.1214/23-BA1381