Statistical Techniques for Particle Physics

21 mai 2008, 11:00 → 22 mai 2008, 12:30 Europe/Paris

Auditorium (LAPP-Annecy)

Kyle Cranmer

This series will consist of two 1.5 hour lectures on statistics for particle physics. 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. In the second lecture, 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) and outline techniques to incorporate background uncertainties. If time allows, I will briefly touch on the statistical challenges of searches for physics beyond the standard model and some ideas for the future.

mer. 21 mai

1 Statistical Techniques for Particle Physics

This series will consist of two 1.5 hour lectures on statistics for particle physics. 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. In the second lecture, 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) and outline techniques to incorporate background uncertainties. If time allows, I will briefly touch on the statistical challenges of searches for physics beyond the standard model and some ideas for the future.

Slides StatisticsLecture_LAPP.pdf

jeu. 22 mai

2 Statistical Techniques for Particle Physics

This series will consist of two 1.5 hour lectures on statistics for particle physics. 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. In the second lecture, 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) and outline techniques to incorporate background uncertainties. If time allows, I will briefly touch on the statistical challenges of searches for physics beyond the standard model and some ideas for the future.