Note that the agenda is provisional
I. Fundamental Concepts
-
Probability and Statistics
Session of 1h30
Lecturer/Intervenants : J. Baudot (IPHC), I. Laktineh (IPNL) -
Fits, Method of chi2 and Method of Maximum Likelihood
Session of 1h30
Lecturer/Intervenant : J. Baudot, I. Laktineh
**
* Statistical tests, limits
* Three sessions of 1h30 (theory and practice)
* Lecturer/Intervenant : Glen Cowan (Royal Holloway, University of London**
**
* Example of application : variance & covariance matrix in a cross-section measurement in nuclear physics
* Session of 1 x 1h30
* Lecturer/ Intervenant : Gregoire Kessedjan (LPSC, INP, IN2P3) II. Mutlivariate Discriminant **
* Discrimination multivariée : introduction théorique
* session of 2 hrs
* Lecturer/Intervenant : Balasz Kegl (LAL, IN2P3)
- **Neural Network and NeuroBayes **
- Session of 2h00
-
Lecturer / Intervenant : **Michael Feindt (Karlsruhe Univ.) **
-
**Neural Network : example of application in top Physics **
- Session of 2h00
-
Lecturer/Intervenant : Dominic Hirshbuehl (Wuppertal Uni.)
-
Boosted Decision Tree and application / Arbres de décision boostés
- Session of 2 hrs
- Lecturer/Intervenant : Yann Coadou (CPPM, IN2P3) ** III. Tools and Framework **
- The RooStat Framework
- Session of 3 hrs.
-
Lecturer/Intervenant : Kyle Cranmer (New York University)
-
** Markov Chain Monte Carlo and application in astroparticule**
- Session of 2 hrs
- Lecturer/Intervenant : **Laurent Derome (LPSC, UJF) **
-
** Bayesian Analysis Tool**
-
Session of 2 hrs
- Lecturer / Intervenant : Daniel Kollar (CERN)
