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Consistent experiment data are crucial to adjust parameters of physics modelsand to determine best estimates of observables. However, often experiment dataare not consistent due to unrecognized systematic errors. Standard methods ofstatistics such as $\chi^2$-fitting cannot deal with this case. Theirpredictions become doubtful and associated uncertainties too small. A human hasthen to figure out the problem, apply corrections to the data, and repeat thefitting procedure. This takes time and potentially costs money. Therefore, aBayesian method is introduced to fit and analyze inconsistent experiment data.It automatically detects and resolves inconsistencies. Furthermore, it allowsto extract consistent subsets from the data. Finally, it provides an overallprediction with associated uncertainties and correlations less prone to thecommon problem of too small uncertainties. The method is foreseen to functionwith a large corpus of data and hence may be used in nuclear databases to dealwith inconsistencies in an automated fashion.

Fitting and Analysis Technique for Inconsistent Nuclear Data