NMAK23002U Computational Methods in Non-life Insurance

Volume 2023/2024

MSc Programme in Actuarial Mathematics


Some Bayesian theory. Standard and Monte Carlo based integration, including Laplace approximation and variational inference. Markov Monte Carlo methods  including Metropolis Hastings and Gibbs sampling. Some classical optimization methods. Simulated annealing and CMA-ES. EM-algorithm. Hyperparameter optimization.

Learning Outcome

Knowledge: Understand the difference between pure Bayesian and frequentist methods. Insight into a number of numerical methods to solve the relevant problems. Knowledge about choosing good hyperparameter values in complex models.

Skills: Be able to identify problems and formulate them mathematically. Also be able to either program the solutions oneself or find relevant programs elsewhere.

Competences: To be able to understand the principles behind the various methods,  including knowledge of their advantages and drawbacks. In addition the students will be able to run and understand the input and output of suitable programs in R. There will also be some focus on how to choose a good computational solution for a given task.


Own notes

NMAB18001U Matematisk statistik (MatStat)
NMAK11022U Regression (Reg) or NMAB22011U Regression for Actuaries (RegAct)
Or similar.

Academic qualifications equivalent to a BSc degree is recommended.
Lectures 5 hours a week. Additional exercise and homework sessions one hour a week.
  • Category
  • Hours
  • Lectures
  • 35
  • Preparation
  • 104
  • Theory exercises
  • 7
  • Project work
  • 40
  • Exam
  • 20
  • Total
  • 206
7,5 ECTS
Type of assessment
Oral examination, 30min
Type of assessment details
Without preparation.
Exam registration requirements

Two compulsory homeworks. They do not count for the grade. 

Without aids
Marking scale
7-point grading scale
Censorship form
External censorship

Oral exam, 30 minutes without preparation. The two  compulsory homeworks have to be turned in no later than three weeks before the reexam.


Criteria for exam assesment

In order to obtain the grade 12 the student should convincingly and accurately demonstrate the knowledge, skills and competences described under Learning outcome.