1st Pan-European Advanced School on Statistics in High Energy Physics

from to (Europe/Berlin)
at DESY Hamburg ( SR 4a/b )
Description

We are looking forward to welcome you to the first Pan-European Advanced School on Statistics in High Energy Physics.  The program consists of lectures on advanced or not so frequently covered topics, supplemented by tutorials. The school is open to PhD students and Post-docs. Participants should already have a good knowledge of basic statistical methods in data analysis.

Covered topics:

  •  Bayesian inference
  •  Machine learning
  •  Unfolding
  •  Gaussian Processes
  •  Non-Parametric Inference

The registration fee is 50 Euro and has to be paid cash.

More information on the INSIGHTS Marie Sklodowska-Curie ITN  can be found here

 

Material:
Contact Email: olaf.behnke@desy.de
Go to day
  • Monday, October 28, 2019
    • 12:30 - 14:00 Registration
    • 14:00 - 18:10 Session 1
      • 14:00 Welcome to School 5'
        Speaker: Dr. Olaf Behnke (DESY)
      • 14:05 Bayesian Inference Lecture I 1h25'
        1) What is probability, leading to derivation of Bayes’ theorem
        2) Comparison of Bayes & Frequentist for the Poisson problem with and w/o
        background, comparison of Bayes with Neyman, Feldman-Cousins
        3) Detailed analysis of the on/off problem 
        4) The role of priors, ways to define them, deciding when they are important
        
        Speaker: Allen Caldwell (MPI Munich)
      • 15:30 Coffee Break 30'
      • 16:00 Bayesian Analysis Toolkit Tutorial I 2h0'
        BAT session 1 will be devoted to brief intro to the Julia programming
        language, installing the package, and solving a simple Poisson problem
        
        Speaker: Dr. Oliver Schulz (MPI Munich)
    • 18:30 - 20:00 Reception
      Location: Canteen Extension Bld. 9
  • Tuesday, October 29, 2019
    • 09:00 - 12:30 Session 2
      • 09:00 Bayesian Inference Lecture II 1h30'
        5) Model testing: the different schools
        6) Detailed comparison and discussion of the Jeffreys’Lindley Paradox
        7) Approaches to mapping the posterior pdf and our scheme for parallelizing
        MCMC
        Speaker: Allen Caldwell (MPI Munich)
      • 10:30 Coffee Break 30'
      • 11:00 Bayesian Analysis Toolkit Tutorial II 1h30'
        BAT session 2 will look at comparing fitting a spectrum and determining
        whether or not a signal is present.
        Speaker: Dr. Oliver Schulz (MPI Munich)
    • 12:30 - 14:00 Lunch Break
    • 14:00 - 18:00 Session 3
      • 14:00 Likelihood Free Inference 1h30'
        General introduction + focus on likelihood-ratio based approaches 
        Speaker: Gilles Louppe (Liege University)
      • 15:30 Coffee Break 30'
      • 16:00 Likelihood-free Inference (Passive) Tutorial 30'
        Speaker: Gilles Louppe (University of Liège)
      • 16:30 Probabilistic Programming 1h30'
         Lecture and (Active) Tutorial on a high energy physics problem
        
        Speaker: Lukas Heinrich (CERN)
  • Wednesday, October 30, 2019
    • 09:00 - 12:30 Session 4
      • 09:00 Generative Models 1h30'
        Lecture:
        - Adversarial Training
        - Generative Models (GAN, WGAN, Autoencoder, Variational Autoencoder)
        - Bonus topics: Bayesian networks, Autoencoders for anomalies
        
        Speaker: Gregor Kasieczka (Institut fuer Experimentalphysik / UHH)
      • 10:30 Coffee Break 30'
      • 11:00 Variational Autoencoder Tutorial 1h30'
        Training generative models on image data
        
        Speaker: Gregor Kasieczka (Institut fuer Experimentalphysik / UHH)
    • 12:30 - 14:00 Lunch Break
    • 14:00 - 18:45 Session 5
      • 14:00 Exemplary usages of GANs in the ATLAS experiment 45'
        Speaker: Michele Faucci Giannelli (Edinburgh)
      • 14:45 Traditional inference with ML tools 1h0'
        Speaker: Lukas Heinrich (CERN)
      • 15:45 Coffee Break 30'
      • 16:15 Approximate Bayesian Computing 1h45'
        Speaker: Chad Shafer (CMU)
    • 19:30 - 22:00 School Dinner
      Location: Canteen Extension Bld. 9
  • Thursday, October 31, 2019
    • 09:30 - 12:15 Session 6
      • 09:30 Decision making under uncertainties" 45'
        In this seminar we present some aspects of the so-called "decision making under uncertainties" 
        that are typical in the context of business and strategical management, where the data represent 
        an important source of information that is not always exploited to the full extent of its potential. 
        In particular, we will describe a few cases taken from the experiences of Pangea Formazione, 
        where the Bayesian approach and more in general Bayesian probabilistic models, when paired
        with suitable data science solutions, have allowed to deal properly with uncertainties and to 
        provide valuable insights about corporate processes.
        Speakers: Fabio Priuli (Pangea Formazione S.r.l.), Sara Borroni (Pangea Formazione S.r.l.)
      • 10:15 Coffee Break 30'
      • 10:45 Gaussian Processes 1h30'
        Speaker: Mikael Kuusela (CMU)
    • 12:15 - 13:30 Lunch Break
    • 13:30 - 18:30 Session 7
      • 13:30 Unfolding 1h30'
        Speaker: Mikael Kuusela (CMU)
      • 15:00 Coffee Break 30'
      • 15:30 RooFitUnfold Tutorial 3h0'
        Speakers: Dr. Lydia Brenner (DESY), Mr. Pim Verschuuren (Royal Holloway, University of London), Carsten Burgard (Nikhef)
  • Friday, November 1, 2019
    • 09:00 - 12:30 Session 8
      • 09:00 Non-Parametric Inference 1h30'
        Speaker: Chad Shafer (CMU)
      • 10:30 Coffee Break 30'
      • 11:00 Non-Parametric Inference 1h20'
        Speaker: Chad Shafer (CMU)
      • 12:20 Closing of the School 10'
        Speaker: Dr. Isabell-A. Melzer-Pellmann (DESY)
    • 12:30 - 14:00 Lunch Break