G.Cowan, Statistical Data Analysis
Notes and didactic material are available at the website:
http://hep.fi.infn.it/ciulli/Site/Analisi_Dati.html
Learning Objectives
Principles of frequentist and Bayesian statistics, and applications to high
energy physics.
C++ and Python programming languages, and the ROOT framework.
Good knowledge of tools for data mining, in particular for data analysis in high energy experiments
Prerequisites
Courses recommended: Nuclear and subnuclear physics
Teaching Methods
CFU: 6
Contact hours for Lectures: 32
Contact hours for Laboratory-field/practice: 16
Further information
Office hours:
on demand
vitaliano.ciulli@fi.infn.it
Website: http://hep.fi.infn.it/ciulli/Site/Analisi_Dati.html
Type of Assessment
Solving of an analysis problem, that requires understanding or writing a
computer program. Oral discussion of the proofs.
Course program
General concepts of statistics. Monte Carlo algorithms and simulations.
Statistical tests and fit techniques. Confidence intervals and limits.
Multivariate analysis. Unfolding.
Event reconstruction in high energy collisions. Data analysis in
elementary particle physics with real-life examples.