Bayesian inference in surface physics

Bayesian data analysis provides a consistent method for the extraction of information from physics experiments. The approach provides a unified rationale for data analysis, which both justifies many of the commonly used analysis procedures and reveals some of the implicit underlying assumptions. This paper introduces the general ideas of the Bayesian probability theory with emphasis on the application to the evaluation of experimental data, namely the deconvolution of the apparatus function for improving the energy resolution, the reconstruction of depth profiles from Rutherford backscattering measurements, handling of discordant data sets and mixture modelling for background estimation of Auger data.

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