Scientific Programme
Probability theory
- Introduction to probability theory
- Bayesian and frequentist approaches to probability
- Random variables, distributions and main properties
Statistical inference
- Parameter estimates, properties of estimators
- Maximum likelihood method
- Pearson and Neyman chi-squares
Confidence intervals and upper limits
- Hypothesis testing
- Goodness of fit
- Frequentist and Bayesian upper limits
Multivairiate discriminators
- Overview of multivariate discrimination methods
- Artificial neural networks
- Boosted decision trees
Statistical software tools
- Overview of the main statistical tools
- RooFit, RooStats
- Usage examples and code demonstrations
Lecturers
- Glen Cowan (Royal Holloway, London)
- Luc Demortier (Rockfeller University, New York)
- Lorenzo Moneta (CERN, Geneva)
- Jochen Ott (KIT, Karlsruhe)
- Harrison Prosper (Florida State University, Tallahassee)
Lectures detailed programme
