Calibrating Bayesian generative machine learning for Bayesiamplification
S Bieringer,
S Diefenbacher,
G Kasieczka
et al.
Abstract:Recently, combinations of generative and Bayesian deep learning have been introduced in particle physics for both fast detector simulation and inference tasks.
These neural networks aim to quantify the uncertainty on the generated distribution originating from limited training statistics.
The interpretation of a distribution-wide uncertainty however remains ill-defined.
We show a clear scheme for quantifying the calibration of Bayesian generative machine learning models.
For a C… Show more
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