240 citations · 1.2k across the 26 of their papers we have counts for
5 papers · 1 filter
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.…
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…
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…
RooStatsCms: a tool for analysis modelling, combination and statistical studies
Danilo Piparo, Gregory Schott, Gunter Quast
RooStatsCms is an object oriented statistical framework based on the RooFit technology. Its scope is to allow the modelling, statistical analysis and combination of multiple search…
RooStatsCms: a tool for analyses modelling, combination and statistical studies
D. Piparo, Gregory Schott, G. Quast
The RooStatsCms (RSC) software framework allows analysis modelling and combination, statistical studies together with the access to sophisticated graphics routines for results visu…