Computational Statistics

Course content

  • Maximum-likelihood and the EM-algorithm.
  • Simulation algorithms and Monte Carlo methods.
  • Markov Chain Monte Carlo.
  • Univariate and multivariate smoothing.
  • Numerical linear algebra in statistics. Sparse and structured matrices.
  • Practical implementation of statistical computations and algorithms.
  • R/C/C++ and RStudio statistical software development.

MSc Programme in Statistics
MSc Programme in Mathematics-Economy

Learning outcome


  • fundamental algorithms for statistical computations
  • R packages that implement some of these algorithms or are useful for developing novel implementations.


Skills: Ability to

  • implement, test, debug, benchmark, profile and optimize statistical software.


Competences: Ability to

  • select appropriate numerical algorithms for statistical computations
  • evaluate implementations in terms of correctness, robustness, accuracy and memory and speed efficiency.

4 hours of lectures per week for 7 weeks.
2 hours of presentation and discussion of a weekly assignment per week for 7 weeks.

Statistik 2 or similar knowledge of statistics and some experience with R usage. Linear algebra, multivariate distributions, likelihood and least squares methods are essential prerequisites.

7,5 ECTS
Type of assessment
Oral examination, 25 min
During the course a total of 6 assignments will be given. At the oral exam one of the 6 assignments is selected at random and the student presents it without preparation. The presentation is followed by a discussion with the examinator within the topics of the course.
Marking scale
7-point grading scale
Censorship form
No external censorship
Two internal examiners.
Criteria for exam assessment

The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 28
  • Exercises
  • 14
  • Exam
  • 1
  • Exam Preparation
  • 30
  • Preparation
  • 133
  • English
  • 206