August 24, 2014 to September 6, 2014
University of Minho and LIP
Europe/Lisbon timezone

Non-parametric density estimation

Sep 5, 2014, 10:41 AM
University of Minho and LIP

University of Minho and LIP

Braga, Portugal


Lucio Anderlini (Laboratoire d'Annecy de Physique de Particules)


Fitting is often used to model data distributions of different categories in order to identify, or unfold, these components in regions of the parameter space where they are mixed. The traditional use of high-order polynomial functions is now being replaced by non-parametric techniques as the Kernel Density Estimation (KDE) and Density Estimation Trees (DETs). Among the advantages of these technique there is the applicability in multi-dimensional, multi-modal datasets. I briefly introduce the concept of non-parametric density estimation, and of the KDE and DET techniques taking examples of applications from recent HEP papers.

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