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Corpus ID: 229363479

Testing whether a Learning Procedure is Calibrated

@inproceedings{Cockayne2020TestingWA,
title={Testing whether a Learning Procedure is Calibrated},
author={Jon Cockayne and M. Graham and Chris J. Oates and T. J. Sullivan},
year={2020}
}

A learning procedure takes as input a dataset and performs inference for the parameters θ of a model that is assumed to have given rise to the dataset. Here we consider learning procedures whose output is a probability distribution, representing uncertainty about θ after seeing the dataset. Bayesian inference is a prime example of such a procedure but one can also construct other learning procedures that return distributional output. This paper studies conditions for a learning procedure to be… CONTINUE READING