HGS-HIRe Power Week 3 - December 2011




December 05-09, 2011.


Introduction into Statistical Methods


Helge Voss, MPI für Kernphysik, Heidelberg

Practical Information


The power week starts with an introduction session on Monday morning and will close on Friday late afternoon.

Late arrivals

If you will arrive late please contact the coordinator in advance.


The power week will be held at Theologisches Seminar Herborn, Nassaustraße 36, 35745 Herborn.


Please arrange for individual travel to the location. We enourage to use car pools.


Accomodation is in double rooms.


Special food requirements (vegetarian, other) will be arranged on the first day.
Full board is included in the lecture week.

What to bring

The power week will be very informal, so only casual attire necessary. Linen and towels will be provided. Please bring sufficient amounts of paper and a laptop if possible.


Wireless LAN is available.


All basic expenses are covered by HGS-HIRe. You only have to pay for local expenses (drinks,etc.). All meals are included.


Though this trip will not generate any costs for your supervisor or group please do not forget to file a trip request so you are covered by insurance during the lecture week.


The program of this statistics power week will be as follows: The lecture series will cover a general introduction into statistics, explaining the basic concepts of probability theory and giving an explanation of the differences between classical(frequentist) and Bayesian statistics. We will introduce random variables, probability densities, expectation values and how this relates to our daily measurements. This will be continued by discussing statistical tests, hypothesis testing and parameter estimation. This will lead us to the discussion of confidence levels, limit and how to incorporate systematic uncertainties. The second half of the week will be devoted to multivariate classification and regression techniques as an application of statistical tools. After introducing the general idea of multivariate classification algorithms, we will get familiar with (Multi-dimensional) Likelihoods, Linear Discriminants, Neural Networks as well as more recent developments of machine-learning tools such as Support Vector Machines and Boosted Decision Trees. While the whole lecture series with accompanying exercises is meant to be self contained without explicit prior knowledge about these statistical concepts, a decent knowledge of C++ and basics concepts of the ROOT data analysis framework (root.cern.ch) will be needed in order to follow the exercises.

What to prepare

A basic knowledge of the ROOT programming language and or C++ is recommended for the lecture week. Below are two tutorials that give a quick introduction.

>Tutorial I >Tutorial II

It is necessary that you bring a laptop with a working ROOT installation (please check before the Power-Week how to set the compiler up, it will save a lot of time during the week.) If you do not have a laptop available or have trouble setting up ROOT, please contact us.


  1. Jordi Albert-Fort
  2. Martha Liliana Cortes
  3. Dennis Döring
  4. Marcel Heine
  5. Jan Kopfer
  6. Matthias Kretz
  7. Igor Kulakov
  8. Vladimir Lavrik
  9. Daniel Löb
  10. Mareike Müller
  11. Valerii Panin
  12. Christopher Pinke
  13. Christian Schäfer
  14. Iurii Sorokin
  15. Tian Zhang
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