A view on model misspecification in uncertainty quantification
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Abstract (arXiv)
Estimating uncertainty of machine learning models is essential to assess the quality of the predictions that these models provide. However, there are several factors that influence the quality of uncertainty estimates, one of which is the amount of model misspecification. Model misspecification always exists as models are mere simplifications or approximations to reality. The question arises whether the estimated uncertainty under model misspecification is reliable or not. In this paper, we argue that model misspecification should receive more attention, by providing thought experiments and contextualizing these with relevant literature.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 20 of its 41 references that are in the map. First posted on arXiv 30 Oct 2022.
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20 of 41 references.
References · 20
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8 more
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Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.