Glen Cowan, Physics Department,
Royal Holloway, University of London, e-mail: firstname.lastname@example.org
The website of the workshop with the contributions by myself (GDC),
and Kyle Cranmer is
here. Below I have just collected
together my parts;
G. Cowan Lecture Materials (approx. by day and still evolving):
The lectures are adapted from a course for postgraduate students
at the University of London. The complete set of lecture notes for
that course plus other resources can be found here.
Problem sheets: Here are some exercises that we will
look at in the afternoon sessions:
on parameter estimation and its solution.
statistical tests and
Problem on multivariate
methods (problem 3 on the sheet).
For the multivariate analysis using TMVA you need
the programs here
(see also the file
problem with ML and profile likelihood (see in particular part e).
Some compute exercises related to
discovery and limits are described here.
These requires the software
SigCalc which you can
get in a single tarball.
Here are some simple programs for working with the roostats
package are SimpleCount and
A set of exercises for optimising a Poisson
counting experiment can be found
here (this includes the
problem sheet and
Some other lecture notes:
- G. Cowan, Topics in statistical data analysis for high energy physics,
- G. Cowan, Statistics for Searches at the LHC,
- G. Cowan, Bayes Factors for
Discovery (draft note).
- G. Cowan, Statistical Data Analysis, Clarendon Press, Oxford, 1998.
- R.J.Barlow, A Guide to the Use of Statistical Methods in the Physical
Sciences, John Wiley, 1989;
- Ilya Narsky and Frank C. Porter, Statistical Analysis Techniques in
Particle Physics, Wiley, 2014;
- 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;
- L.Lyons, Statistics for Nuclear and Particle Physics, CUP, 1986.
You can also download the sections on
(pdf files) from the Review of Particle Physics by the
Particle Data Group.