Statistical Data Analysis
Royal Holloway, University of London,
phone: (01784) 44 3452, e-mail: email@example.com
Time & Place:
The 2020/21 lectures take place online (videos below; discussion
sessions Mondays 11, 3 or 5).
Moodle page: U. of London MSc and MSci students should access the
course through its RHUL
Course structure: For 2020/21, as in recent years,
the computing element of the course will not be assessed.
Nevertheless, some of the statistical methods will be practiced
using computer programs in python or C++. Students
should have some familiarity with at least one of these languages
or be willing to use additional resources to acquire the needed
computing skills. In contrast to previous years, there will be no
specific tuition provided in C++.
Aims: This series of lectures
is intended for PhD students in Particle Physics and it also forms
the University of London MSci course PH4515. The purpose of the lectures
on probability and statistics is to
present the basic mathematical tools needed for the analysis of
experimental data. The methods will be practiced by writing and
running short computer programs.
Although the examples used in the course often relate to particle
physics this is done in a relatively simple way and MSci students from
other physics areas should not find this too great a difficulty.
Syllabus: A general
outline of the course topics.
Lecture videos and slides:
Problem sheets: There are 9 problem sheets due on Mondays
from weeks 3 through 11. Further info on these can
be found in the slides for week 1 and part 1 of the corresponding video.
Books on statistical methods:
- The course mainly follows: G. Cowan, Statistical Data Analysis,
Clarendon Press, Oxford, 1998; more here.
- R.J.Barlow, A Guide to the Use of Statistical Methods in the Physical
Sciences, John Wiley, 1989;
- Frederick James, Statistical Methods in Experimental Physics,
2nd edition, World Scientific 2006;
- S.Brandt, Statistical and Computational Methods in Data
Analysis, Springer, New York, 1998;
- Ilya Narsky and Frank Porter, Statistical Analysis
Techniques in Particle Physics, Wiley, 2013.
- L.Lyons, Statistics for Nuclear and Particle Physics, CUP, 1986.
Books on multivariate methods:
Christopher Bishop, Pattern Recognition and Machine Learning,
- T. Hastie, R. Tibshirani and J. Friedman,
The Elements of
Statistical Learning, 2nd edition, Springer, 2009.
- Gareth James, Daniela
Witten, Trevor Hastie and Robert Tibshirani, An Introduction to
Statistical Learning with Applications in R:
Some additional notes/resources:
Some more lectures on statistics I've given:
- Academic training lectures
Statistics at the LHC, CERN, 14-17 June, 2010.
statistical methods for particle physics at Tsinghua University,
12-16 April, 2010.
- Seminar on
recent progress in multivariate methods for particle physics,
Weizmann Institute of Science, 17 Jan 2010.
- Statistical Methods in Particle Physics at SUSSP65,
St Andrews, 16-29 August 2009:
- Two lectures on Bayesian methods given at the DESY
School (part of the Helmholtz Alliance Physics at the Terascale initiative),
29 September - 2 October 2008:
lecture 1 (ppt,pdf),
lecture 2 (ppt,pdf).
- The materials for my lectures on
advanced statistical methods for data
analysis (multivariate methods) for the University of Mainz
(Klausurtagung des GK "Eichtheorien -- experimentelle Tests...",
Bullay/Mosel, 15-17 September, 2008). These are updated versions of
my lectures on multivariate statistical
methods in particle physics (CERN Academic Training Lectures,
16-19 June, 2008).
- The CERN Summer Student Lecture statistics lectures are
- Bayesian statistics
at the LHC (and elsewhere), Cavendish-DAMTP HEP phenomenology seminar,
Cambridge, 7 March 2008.
- Lectures on Statistics at the CERN-FNAL Hadron Collider Physics School:
lecture 1 and
CERN, 6 and 8 June, 2007.
- Bayesian statistical methods
for parton analyses, talk at DIS2006, Tsukuba, 22 April, 2006.
Archives -- Statistical Data Analysis old lectures:
Information on computing setup: Some info on how to log into
the RHUL particle physics linux machine linappserv1 from the teaching
lab or your own computer is
Once you have your account on linappserv3 you connect from any other
networked linux machine with
ssh -X firstname.lastname@example.org
where for "username" you substitute your login name, and then enter
your password. You will have been given information on computer
security and on how to change your password. It is your
responsibility to read and follow these rules.
The -X qualifier above should allow you to open up an "x-window". You can
check this by typing at the prompt
which should open up a clock in a small window. If it doesn't work, try
using -Y or -XY.
Your default shell is bash. Your account should have in the
home directory a file called .bash_profile (check this with
ls -la). If it isn't there, you can copy this
.bash_profile to your
home directory. This defines certain aliases and environment
variables automatically when you log in. In particular, it
defines the environment variable ROOTSYS, which you need for
the ROOT programs we will use.
You can also copy to your home directory the file
.emacs, which will
set some defaults for the emacs editor.