MATH5835M — Statistical Computing

Syllabus

This module discusses a variety of circumstances in which computational methods play a key role in data analysis. Computers (particularly the R package) are used intensively, but there is also a theoretical and methodological rationale. The following topics will be covered.

• random number generation
• Monte-Carlo methods
• Resampling methods
• Markov Chain Monte Carlo (MCMC) methods
• EM algorithm
• implementation in R

The presentation of the module is self-contained, i.e. it will not be necessary to buy/borrow a book for the module. Printed copies of the lecture notes will be distributed in the first week; the lecture notes are also available online.

Time Table

There will be 27 lectures (L1 - L27) and 6 example classes (E1 - E6). The schedule is given in the following table.

All lessons will take place in Roger Stevens LT 01 (7.01).

Handouts

Software

For the module we will use the statistical computing package R. This program is free software, and you can find the program and documentation at the R project homepage. Useful resources for learning R include to following:

• The slides from the induction week.
• The R manual.
• The R online help, accessed by typing help() or help.start() in R.
• The departmental web page has a list with some R tutorials.

Links

• MATH5835M Campusweb page
• The university timetable/room plan
• past exam papers