Terascale Statistics School 2017

6 Mar 2017, 14:00 → 10 Mar 2017, 18:00 Europe/Berlin

SR 4a/b (DESY Hamburg)

Lectures on statistics in high-energy physics:

- Basic concepts: Roger Barlow
- Advanced topics: Glen Cowan
- Machine learning: Harrison Prosper
- Introduction to R: Wolfgang Rolke
- Goodness of fit: Wolfgang Rolke
 

Excercises:

- Parameter estimates
- Likelihood methods
- Limit Setting
- Machine learning

It is expected that you can bring your own laptop for the excercises. Basic knowledge of Python is expected for the machine learning exercises, if you do not have any experience, please prepare yourself at least with a few online tutorials.

The school fee is 50 Euro.

Please register until 17 February 2017.

Monday 6 March

14:00 → 14:10 Welcome

Slides Welcome_Statistics2017.pdf

14:10 → 15:40 Introduction

Speaker: Prof. Roger Barlow (Huddersfield University)

Slides Terascale-Barlow1.pdf

15:40 → 16:10 Coffee break

16:10 → 17:40 Parameter estimation

Speaker: Prof. Glen Cowan (Royal Holloway, University of London)

Slides cowan_desy_2017.pdf

17:40 → 18:00 Computing set up

Slides NAF-statistics-2017.pdf

Tuesday 7 March

09:00 → 10:30 Introduction (Hypothesis Testing and Confidence Intervals)

Speaker: Prof. Roger Barlow (Huddersfield University)

Slides Terascale-Barlow2.pdf

10:30 → 11:00 Coffee break

11:00 → 12:30 Parameter estimation

Speaker: Prof. Glen Cowan (Royal Holloway, University of London)

12:30 → 14:00 Lunch break

14:00 → 15:30 Tutorial - Limits (I)

Speaker: Dr Roger Wolf (Karlsruhe Institut für Technologie (KIT))

exercise-macros.tar.gz exercise-macros.tar.gz

Slides Terascale-2017-statistics.pdf

15:30 → 16:00 Coffee break

16:00 → 18:00 Tutorial - Limits (II)

Speaker: Dr Roger Wolf (Karlsruhe Institut für Technologie (KIT))

Wednesday 8 March

09:00 → 10:30 Introduction (III)

Speaker: Prof. Roger Barlow (Huddersfield University)

Slides Terascale-Barlow3.pdf

10:30 → 11:00 Coffee break

11:00 → 12:30 Introduction: Machine learning

Speaker: Prof. Harrison Prosper (Florida State University)

Slides DESY2017_Prosper1.pdf

12:30 → 14:00 Lunch break

14:00 → 15:30 Tutorial: Machine learning

Speaker: Prof. Harrison Prosper (Florida State University)

15:30 → 16:00 Coffee break

16:00 → 18:00 Tutorial: Machine learning

Speaker: Prof. Harrison Prosper (Florida State University)

18:00 → 21:00 Dinner

Thursday 9 March

09:00 → 10:30 Introduction to R

Speaker: Wolfgang Rolke (UPR)

Paper Introduction__to_R.pdf

RData file RDesy.RData

Slides Introduction__to_R.pptx

10:30 → 11:00 Coffee break

11:00 → 12:30 Machine learning (II)

Speaker: Prof. Harrison Prosper (Florida State University)

Slides DESY2017_Prosper2.pdf

12:30 → 14:00 Lunch break

14:00 → 15:30 Tutorial: Machine learning (II)

Speaker: Prof. Harrison Prosper (Florida State University)

15:30 → 16:00 Coffee break

16:00 → 18:00 Tutorial: Machine learning (II)

Speaker: Prof. Harrison Prosper (Florida State University)

Friday 10 March

09:00 → 10:30 Goodness of fit

Speaker: Wolfgang Rolke (UPR)

Paper Testing_Goodness_of_Fit.pdf

Slides Testing_Goodness_of_Fit.pptx

10:30 → 10:45 Coffee break

10:45 → 11:45 Highlight talk: Statistics in the past, present and future

Speaker: Prof. Harrison Prosper (Florida State University)

11:45 → 11:55 Goodbye