NMAK16005U Computational Statistics

Volume 2026/2027
Education

MSc Programme in Mathematics-Economics
MSc Programme in Statistics
MSc Programme in Actuarial Mathematics

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
  • Statistical software development in R and C++
Learning Outcome

Knowledge:

  • 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.

See Absalon for a list of course literature.

Previously, the course has used the following literature:

  • Hansen, N. R. (2024, August 9). Computational statistics with R [Online Book]. Computational Statistics with R. https:/​/​cswr.nrhstat.org/​

  • Wickham, H. (2019). Advanced R (2nd ed.). CRC Press, Taylor & Francis Group. https://adv-r.hadley.nz

Both books are available online for free.

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.
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.
  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 119
  • Exercises
  • 28
  • Exam Preparation
  • 30
  • Exam
  • 1
  • Total
  • 206
Continuous feedback during the course of the semester
Credit
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.
Examination prerequisites

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

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

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 assesment

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