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"Statistical Techniques for Particle Physics" (4/4)

<!--HTML-->This series will consist of four 1-hour lectures on statistics for particle physics. The goal will be to build up to techniques meant for dealing with problems of realistic complexity while maintaining a formal approach. I will also try to incorporate usage of common tools like RO...

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Detalles Bibliográficos
Autor principal: Kyle Cranmer
Lenguaje:eng
Publicado: 2009
Materias:
Acceso en línea:http://cds.cern.ch/record/1159900
Descripción
Sumario:<!--HTML-->This series will consist of four 1-hour lectures on statistics for particle physics. The goal will be to build up to techniques meant for dealing with problems of realistic complexity while maintaining a formal approach. I will also try to incorporate usage of common tools like ROOT, RooFit, and the newly developed RooStats framework into the lectures. The first lecture will begin with a review the basic principles of probability, some terminology, and the three main approaches towards statistical inference (Frequentist, Bayesian, and Likelihood-based). I will then outline the statistical basis for multivariate analysis techniques (the Neyman-Pearson lemma) and the motivation for machine learning algorithms. Later, I will extend simple hypothesis testing to the case in which the statistical model has one or many parameters (the Neyman Construction and the Feldman-Cousins technique). From there I will outline techniques to incorporate background uncertainties. If time allows, I will touch on the statistical challenges of searches for physics beyond the standard model and the look-elsewhere effect.