This course gives an introduction to advanced topics in statistical computation. Prior experience
of programming in the R language will be assumed. A selection of topics included is:
Simulation of random variables
Computationally intensive methods: Bootstrap, Expectation Maximisation
Develop programming skills and knowledge of methodology for Statistical Computing
Mode of Instruction
Lectures and (computer) assignments in the computer language of the statistical package R, to be downloaded from the R-project site http://www.r-project.org. It is free!
For the course days, course location and class hours check the Time Table under the tab “StatSci Students -> Program Schedule” at http://www.math.leidenuniv.nl/statisticalscience
By weekly reports (1/2 of final grade) and a final open book written exam (1/2 of final grade). To pass the course, a grade of 5 on both components is required.
Date information about the exam and resit can be found in the Time Table pdf document under the tab “Masters Programme” at http://www.math.leidenuniv.nl/statscience. The room and building for the exam will be announced on the electronic billboard, to be found at the opposite of the entrance, the content can also be viewed here http://info.liacs.nl/math/.
Enroll in Blackboard for the course materials and course updates.
To be able to obtain a grade and the ECTS for the course, sign up for the (re-)exam in uSis ten calendar days before the actual (re-)exam will take place. Note, the student is expected to participate actively in all activities of the program and therefore uses and registers for the first exam opportunity.
Exchange and Study Abroad students, please see the Prospective students website for information on how to apply.
Statistical computing with R, by Maria L. Rizzo, Chapman and Hall.
Background knowledge of R:
The art of R programming, Norman Matloff, No Starch Press 2011, ISBN: 978-1-59327-384-2
Steven de Rooij: email@example.com
- This is a compulsory course in the Master’s programme of the specialisations Data Science and Statistical Science for the Life & Behavioural sciences.