On the validity of using the delta method for calculating the uncertainty of the predictions from an overparameterized model
CoRR(2023)
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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