Speaker
Giovanni De Crescenzo
Description
Neural Networks coupled with a Monte Carlo method can be used to perform regression in the presence of incomplete information. A methodology based on this idea has been developed for the determination of parton distributions, and a closure testing methodology can be used in order to verify the reliability of the uncertainty in the results.
A relevant question in this context is what happens if the uncertainty of the input data is incorrectly estimated in the first place. We investigate this issue by a suitable adaptation of the closure testing methodology.
