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Multivariate statistical methods and data mining in particle physics (4/4)

<!--HTML-->The lectures will cover multivariate statistical methods and their applications in High Energy Physics. The methods will be viewed in the framework of a statistical test, as used e.g. to discriminate between signal and background events. Topics will include an introduction to the...

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Autor principal: Glen COWAN
Lenguaje:eng
Publicado: 2008
Materias:
Acceso en línea:http://cds.cern.ch/record/1111146
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author Glen COWAN
author_facet Glen COWAN
author_sort Glen COWAN
collection CERN
description <!--HTML-->The lectures will cover multivariate statistical methods and their applications in High Energy Physics. The methods will be viewed in the framework of a statistical test, as used e.g. to discriminate between signal and background events. Topics will include an introduction to the relevant statistical formalism, linear test variables, neural networks, probability density estimation (PDE) methods, kernel-based PDE, decision trees and support vector machines. The methods will be evaluated with respect to criteria relevant to HEP analyses such as statistical power, ease of computation and sensitivity to systematic effects. Simple computer examples that can be extended to more complex analyses will be presented.
id cern-1111146
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2008
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spelling cern-11111462022-11-03T08:16:11Zhttp://cds.cern.ch/record/1111146engGlen COWANMultivariate statistical methods and data mining in particle physics (4/4)Multivariate statistical methods and data mining in particle physics (4/4)Academic Training Lecture Regular Programme<!--HTML-->The lectures will cover multivariate statistical methods and their applications in High Energy Physics. The methods will be viewed in the framework of a statistical test, as used e.g. to discriminate between signal and background events. Topics will include an introduction to the relevant statistical formalism, linear test variables, neural networks, probability density estimation (PDE) methods, kernel-based PDE, decision trees and support vector machines. The methods will be evaluated with respect to criteria relevant to HEP analyses such as statistical power, ease of computation and sensitivity to systematic effects. Simple computer examples that can be extended to more complex analyses will be presented.oai:cds.cern.ch:11111462008
spellingShingle Academic Training Lecture Regular Programme
Glen COWAN
Multivariate statistical methods and data mining in particle physics (4/4)
title Multivariate statistical methods and data mining in particle physics (4/4)
title_full Multivariate statistical methods and data mining in particle physics (4/4)
title_fullStr Multivariate statistical methods and data mining in particle physics (4/4)
title_full_unstemmed Multivariate statistical methods and data mining in particle physics (4/4)
title_short Multivariate statistical methods and data mining in particle physics (4/4)
title_sort multivariate statistical methods and data mining in particle physics (4/4)
topic Academic Training Lecture Regular Programme
url http://cds.cern.ch/record/1111146
work_keys_str_mv AT glencowan multivariatestatisticalmethodsanddatamininginparticlephysics44