A View on Model Misspecification in Uncertainty Quantification

Conference paper (2023)

Authors

Y. Kato Pattern Recognition and Bioinformatics -

David Tax Pattern Recognition and Bioinformatics -

M. Loog Pattern Recognition and Bioinformatics - , Radboud Universiteit Nijmegen

Research Group

Pattern Recognition and Bioinformatics () (TU Delft)

DOI: https://doi.org/10.1007/978-3-031-39144-6_5

Uncertainty quantification Epistemic and Aleatoric uncertainty Model misspecification

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Published Date

2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

Abstract

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.

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