From chi^2 to Bayesian model comparison : the example of Levy-based fits to e+e- correlations

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dc.contributor.authorDe Kock, Michiel B.en_ZA
dc.contributor.authorEggers, Hans C.en_ZA
dc.contributor.authorCsorgo, Tamasen_ZA
dc.date.accessioned2013-07-03T08:34:51Z
dc.date.available2013-07-03T08:34:51Z
dc.date.issued2012
dc.descriptionCITATION: De Kock, M. B., Eggers, H. C. & Csorgo, T. 2012. From chi^2 to Bayesian model comparison : the example of Levy-based fits to e+e- correlations. In The Seventh Workshop on Particle Correlations and Femtoscopy, September 20th to 24th, 2011 The University of Tokyo, Japan.
dc.descriptionThe original publication is available at https://pos.sissa.it/154
dc.description.abstractThe usual χ2 method of fit quality assessment is a special case of the more general method of Bayesian model comparison which involves integrals of the likelihood and prior over all possible values of all parameters. We introduce new parametrisations based on systematic expansions around the stretched exponential or Fourier-transformed Lévy source distribution, and utilise the increased discriminating power of the Bayesian approach to evaluate the relative probability of these models to be true representations of a recently measured Bose-Einstein correlation data in e +e− annihilations at LEP.
dc.description.urihttps://pos.sissa.it/154/033/
dc.description.versionPublisher's version
dc.format.extent7 pages
dc.identifier.citationDe Kock, M. B., Eggers, H. C. & Csorgo, T. 2012. From chi^2 to Bayesian model comparison : the example of Levy-based fits to e+e- correlations. In The Seventh Workshop on Particle Correlations and Femtoscopy, September 20th to 24th, 2011 The University of Tokyo, Japan.
dc.identifier.urihttp://hdl.handle.net/10019.1/84721
dc.language.isoen
dc.publisherProceedings of Science
dc.rights.holderAuthors retain copyright
dc.subjectBayesian statistical decision theoryen_ZA
dc.titleFrom chi^2 to Bayesian model comparison : the example of Levy-based fits to e+e- correlationsen_ZA
dc.typeConference Paper
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