Computational Statistics

Course content

  • Maximum-likelihood and numerical optimization.
  • The EM-algorithm.
  • Stochastic optimization algorithms.
  • Simulation algorithms and Monte Carlo methods.
  • Nonparametric density estimation.
  • Bivariate smoothing.
  • Numerical linear algebra in statistics. Sparse and structured matrices.
  • Practical implementation of statistical computations and algorithms.
  • R/C++ and RStudio statistical software development.

MSc Programme in Mathematics-Economics
MSc Programme in Statistics

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 the exam assignments per week for 7 weeks.
2 hours of exercises per week for 7 weeks.

StatMet and MStat (alternatively MatStat from previous years) or similar knowledge of statistics and some experience with R usage. Linear algebra, multivariate distributions, likelihood and least squares methods are essential prerequisites. It is a good idea to have a working knowledge of conditional distributions as treated in Statistics A.

Academic qualifications equivalent to a BSc degree is recommended.

This course requires a certain statistical maturity at the level of MSc students in statistics. It is not an introduction to R for statistical data analysis.

Continuous feedback during the course of the semester
7,5 ECTS
Type of assessment
Oral examination, 25 minutes
Type of assessment details
During the course a total of eight assignments will be given within four different topics. The student needs to select one assignment from each topic and prepare a solution of that assignment for the exam. That is, the student needs to prepare the solution of four assignments in total.

At the oral exam one assignment out of the four prepared by the student 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. The grade is based on the oral presentation and the following discussion.
Exam registration requirements

To participate in the final oral exam one oral presentation must have been given during the course.

All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Two internal examiners.

Same as ordinary exam. To be eligible for the re-exam, students who did not give an oral presentation during the course must hand in synopses of the solutions of four assignments. The four synopses must be approved no later than three weeks before the beginning of the re-exam week.

Criteria for exam assessment

The student should convincingly and accurately demonstrate the knowledge, skills and competences described under Intended learning outcome.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 119
  • Exercises
  • 28
  • Exam Preparation
  • 30
  • Exam
  • 1
  • English
  • 206


Course number
7,5 ECTS
Programme level
Full Degree Master

1 block

Block 1
No limit
The number of seats may be reduced in the late registration period
Study Board of Mathematics and Computer Science
Contracting department
  • Department of Mathematical Sciences
Contracting faculty
  • Faculty of Science
Course Coordinator
  • Dmytro Marushkevych   (8-4c75817c7a7736754875697c7036737d366c73)
Saved on the 28-02-2023

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