On the validity of using the delta method for calculating the uncertainty of the predictions from an overparameterized model

CoRR(2023)

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摘要
The uncertainty in the prediction calculated using the delta method for an over-parameterized (parametric) black-box model is shown to be larger or equal to the uncertainty in the prediction of a canonical (minimal) model. Equality holds if the additional parameters of the overparameterized model do not add flexibility to the model. As a conclusion, for an overparameterized black-box model, the calculated uncertainty in the prediction by the delta method is not underestimated. The results are shown analytically and are validated in a simulation experiment where the relationship between the normalized traction force and the wheel slip of a car is modelled using e.g., a neural network.
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关键词
Machine learning,nonlinear system identification,overparameterized model,uncertainty quantification,neural networks,autonomous vehicles
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