Speaker
Description
We present the application of Makov Chain Monte Carlo (MCMC) method to analysis of parton distribution functions (PDFs). The MCMC approach naturally implements Bayes' theorem, hence provides a means to directly sample the underlying probability distribution - in this case the probability distribution of the PDF parameters. This allows for a straightforward propagation of the resulting uncertainties into any PDF-dependent observable, preserving their simple probabilistic interpretation. We show that the flexibility of the Bayes framework, allowing e.g. to account for non-Gaussianity, inconsistencies of data sets, or multiple minima, is crucial to extract realistic uncertainties when such assumptions are not fulfilled. The method is successfully applied in two cases: to determine proton and nuclear PDFs.
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