Statistical Techniques for Particle Physics
published: Sept. 10, 2010, recorded: February 2009, views: 448
released under terms of: Creative Commons Attribution Non-Commercial Share Alike (CC-BY-NC-SA)
Download slides: cernacademictraining09_cranmer_stpp_01.pdf (4.2 MB)
Download cernacademictraining09_cranmer_stpp_01.mp4 (Video - generic video source 671.5 MB)
Download cernacademictraining09_cranmer_stpp_01.flv (Video 300.3 MB)
Download cernacademictraining09_cranmer_stpp_01.wmv (Video 303.9 MB)
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !