Markov Chain Monte Carlo techniques applied to Parton Distribution Functions determination: proof of concept

Abstract : We propose a Bayesian parameter inference approach to determine Parton Distribution Functions (PDFs) and we show that we can replace the standard c2 minimisation used in most existing PDF global analysis procedures, by Markov chain Monte Carlo (MCMC) techniques. These methods, widely used in statistics, lead to reliable estimates of uncertainties in terms of confidence limit intervals of probability distributions, and offer additional insight into the rich field of PDFs. The formulation of PDF determination in terms of Bayesian inference, the Monte Carlo algorithm we have implemented in the xFitter code and a selection of first results we have obtained are presented in this contribution.
Type de document :
Communication dans un congrès
25th International Workshop on Deep Inelastic Scattering and Related Topics (DIS 17), Apr 2017, Birmingham, United Kingdom. Proceedings of Science, pp.213, 2017, DIS2017. 〈https://indico.cern.ch/event/568360〉
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http://hal.in2p3.fr/in2p3-01501895
Contributeur : Emmanuelle Vernay <>
Soumis le : mardi 4 avril 2017 - 16:49:21
Dernière modification le : mardi 22 mai 2018 - 21:48:11

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  • HAL Id : in2p3-01501895, version 1

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M. Mangin-Brinet, Y.G. Gbedo. Markov Chain Monte Carlo techniques applied to Parton Distribution Functions determination: proof of concept. 25th International Workshop on Deep Inelastic Scattering and Related Topics (DIS 17), Apr 2017, Birmingham, United Kingdom. Proceedings of Science, pp.213, 2017, DIS2017. 〈https://indico.cern.ch/event/568360〉. 〈in2p3-01501895〉

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