Advanced statistical methods for data analysis
Description of lectures: The lectures will focus on 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.
Lecture Notes in pdf format (preliminary -- subject to minor changes):
The code used to make some of the simple examples with the TMVA package can be found here.
According to time and interest we might also want to talk about some applications of Bayesian statistics in particle physics. Here is a recent seminar on Bayesian methods in HEP we can look at.
More introductory material on computing and statistical methods in particle physics can be found from my University of London course here.