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Advanced Statistical Computing


Admission requirements



This course gives an introduction to advanced topics in statistical computation. Prior experience
of programming in the R language will be assumed; we will however meet limitations of R and
occasionally need to look outside of R for suitable tools. A selection of topics included is:

  • Simulation of random variables

  • Sampling distributions and power functions.

  • Visualization, Monte Carlo Integration

  • Numerically intensive methods: Bootstrap, EM, MCMC, Bayes’ nets.

  • Numerical Algorithms

Course objectives

Develop programming skills 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 It is free!

Time Table

For the course days, course location and class hours check the Time Table 2014-15 under the tab “Masters Programme” at

Assessment method

By weekly reports (2/3 of final grade) and a final open book written exam (1/3 of final grade)

Date information about the exam and resit can be found in the Time Table 2014-15 pdf document under the tab “Masters Programme” at The exams take place in the Snellius building, the room will be announced on the electronic billboard, to be found at the opposite of the entrance, the content can also be viewed online at:“”:

If the exam does not take place in the Snellius building, then an announcement will be sent via blackboard.


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.

Reading list

  • 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

Contact information

gill [at] math [dot] leidenuniv [dot] nl


  • This is a compulsory course in the Master’s programme of the specialisation Statistical Science for the Life & Behavioural sciences.