Computational Statistics

Course content

  • Maximum-likelihood and numerical optimization
  • The EM-algorithm.
  • Simulation algorithms and Monte Carlo methods.
  • 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.

Continuous feedback during the course of the semester
7,5 ECTS
Type of assessment
Oral examination, 25 minutes
During the course a total of eight assignments will be given. Four of these must be solved, and at the oral exam one assignment out of the four is selected at random for presentation by the student. The oral exam is without preparation. The presentation is followed by a discussion with the examinator within the topics of the course.
All aids allowed
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