Handling discordant data sets
R. Preuss · AIP conference proceedings · 2001
Experimental data from different sources may suffer from discordant calibrations and possibly covers different regions of the independent variables. A model function spanning the complete range has to account for all available data. In our approach one of the data sets is taken as correct on the absolute scale, while in the other data sets we allow for an unknown scale factor. Bayesian probability theory is employed to evaluate the unknown scale factors and the model parameters.