This course aims at providing an introduction to the most commonly used statistical methods for high energy physics.
Morning lectures will be supplemented by afternoon exercise sessions, where the material of the morning will be put in practice.
This course will provide 3 credits for the UCLouvain doctoral school (CDD Sciences).
COVID-19 measures: the course will take place in room E349 at CP3 and online (the zoom link will be sent to the registered participants by email).
The evolution is governed by the three colour-codes of the university. Please do not make any travel arrangement until we get back to you about this.
In case of Code Yellow, the course will be held in a hybrid in-erson/online way. The room will host at most 8-10 participants: priority will be given to PhD students.
In case of Code-Orange (the current status) the course will be held fully online (the room would host 4-5 people, and it would therefore make much more sense to do it online only.
In case of code-red the course will be of course held fully online.
If, as any good Bayesian, I had to bet, given the current situation and its likely evolution, I would bet the course will be held fully online.
Tentative program (subject to changes):
Lesson 1 - Fundaments: Bayesian and frequentist probability, theory of measure, correlation and causality, distributions
Lesson 2 - Point and Interval estimation: maximum likelihood methods, confidence intervals, most probable values, credible intervals
Lesson 3 - Test of hypotheses: frequentist and Bayesian tests, CLs, significance, look-elsewhere effect, reproducibility crysis
Lesson 4 - Commonly-used methods in particle physics: unfolding, ABCD, ABC, MCMC
Lesson 5 - Machine Learning: overview and mathematical foundations, generalities most used algorithms, automatic Differentiation and Deep Learning
Pietro Vischia (teacher and chair)
Carinne Mertens (secretariat)