3 papers
physics.data-an2020
Optimal statistical inference in the presence of systematic uncertainties using neural network optimization based on binned Poisson likelihoods with nuisance parameters
Stefan Wunsch, Simon Jörger, Roger Wolf +1
Data analysis in science, e.g., high-energy particle physics, is often subject to an intractable likelihood if the observables and observations span a high-dimensional input space.…
physics.data-an2019
Reducing the dependence of the neural network function to systematic uncertainties in the input space
Stefan Wunsch, Simon Jörger, Roger Wolf +1
Applications of neural networks to data analyses in natural sciences are complicated by the fact that many inputs are subject to systematic uncertainties. To control the dependence…
physics.data-an2018
Identifying the relevant dependencies of the neural network response on characteristics of the input space
Stefan Wunsch, Raphael Friese, Roger Wolf +1
The relation between the input and output spaces of neural networks (NNs) is investigated to identify those characteristics of the input space that have a large influence on the ou…