Home
Computational Methods for Statistics with Applications
General motivation
The aim of the course is to enable students to understand and use
important techniques in Computational Statistics. The students
will study computational methods used in Statistics with emphasis
on large-scale computations and will develop understanding and
skills how to use these appropriately in research and
applications. The importance of how to efficiently implement those
methods on the
available parallel and high performance computer (HPC) facilities is to
be particularly emphasized.
This course is intended for students and researchers in various fields where statistics is used as a modelling and analysis tool.
The students will study computational methods used in statistics with emphasis on large-scale computations and will develop an understanding
of, as well as the skills for how to use these appropriately in research and applications.
Dates, places and course pages
- Week 35: Private studies.
- Week 36: Lectures held in Uppsala
- Week 37: Homework.
- Week 38: Lectures held in Uppsala
- Week 39: Project work at home.
Review material for Week 35's self study:
In order to follow the material, the following basic concepts and definitions from Statistics and Linear Algebra have to be reviewed.- Linear systems, rank, singular matrix, non-singular matrix,
tridiagonal (band) matrix, LU, and Gaussian elimination.
Read:- Eldén, Wittmeyer-Koch, Bruun-Nielsen, Introduction to numerical computations, Studentlitteratur, 2001, (here)
- (Wikipedia source) or some other introductor text.
- Basic knowledge of stochastic variables, statistical
distributions. Read
- (Wikipedia source)
- or some other introductor text.
- Those who want to learn R well and use it in the course, should
carefully study one of the two suggested books ([1] or [2])
Recommended books (to be updated) - Peter Dalgaard, Introductory Statistics with R, Springer, 2002.
- W.John Braun, Duncan J. Murdoch, A First Course in Statistical Programmimg with R, Cambridge University Press, 2007.
- Geof H. Givens and Jennifer A. Hoeting, Computational Statistics, Wiley, 2005.
- Wendy L. Martinez and Angel R. Martinez, Computational Statistics Handbook with MATLAB, Chapman & Hall/CRC, 2002.
- Lars Eldén. Matrix Methods in Data Mining and Pattern Recognition. SIAM, Philadelphia, PA, Philadelphia, PA, USA, 2007.
Schedule
A schedule for the course can be found here
Registration
You register for the course by sending an e-mail to Maya Neytcheva: maya.neytcheva@it.uu.se
Deadline for registration is June 17.
Organization issues:
Some instructions how to find us in Uppsala are to be found here .
Suggested hotel to book rooms in Uppsala:
Hotel Uppsala .
For NGSSC students only:
Your home department is expected to provide advance payment for travel and housing. After the course has been completed, the costs will be reimbursed from NGSSC by a lump grant of SEK 12 000.
