Statistical Techniques for Particle Physics

author: Kyle Cranmer, CERN - European Organization for Nuclear Research
published: Sept. 10, 2010,   recorded: February 2009,   views: 449
released under terms of: Creative Commons Attribution Non-Commercial Share Alike (CC-BY-NC-SA)
Categories

See Also:

Download slides icon Download slides: cernacademictraining09_cranmer_stpp_01.pdf (4.2 MB)

Download Video - generic video source Download cernacademictraining09_cranmer_stpp_01.mp4 (Video - generic video source 671.5 MB)

Download Video Download cernacademictraining09_cranmer_stpp_01.flv (Video 300.3 MB)

Download Video Download cernacademictraining09_cranmer_stpp_01.wmv (Video 303.9 MB)


Help icon Streaming Video Help

Related Open Educational Resources

Related content

Report a problem or upload files

If 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.
Lecture popularity: You need to login to cast your vote.
  Bibliography

 Watch videos:   (click on thumbnail to launch)

Watch Part 1
Part 1 1:11:58
!NOW PLAYING
Watch Part 2
Part 2 1:00:59
!NOW PLAYING
Watch Part 3
Part 3 1:00:15
!NOW PLAYING
Watch Part 4
Part 4 1:08:37
!NOW PLAYING

Description

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 page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Reviews and comments:

Comment1 Jon, January 15, 2019 at 7:37 a.m.:

the http://google.com

Write your own review or comment:

make sure you have javascript enabled or clear this field: