Computational Methods in Non-life Insurance
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.
MSc Programme in Actuarial Mathematics
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.
Lectures 5 hours a week. Additional exercise and homework sessions one hour a week.
NMAB18001U Matematisk statistik (MatStat)
NMAK11022U Regression (Reg) or NMAB22011U Regression for Actuaries (RegAct)
Academic qualifications equivalent to a BSc degree is recommended.
- 7,5 ECTS
- Type of assessment
Oral examination, 30min
- Type of assessment details
- Without preparation.
- Without aids
- Marking scale
- 7-point grading scale
- Censorship form
- External censorship
Criteria for exam assessment
In order to obtain the grade 12 the student should convincingly and accurately demonstrate the knowledge, skills and competences described under Learning outcome.
Part time Master and Diploma courses
- Theory exercises
- Project work
- Course number
- 7,5 ECTS
- Programme level
- Full Degree Master
- Block 4
- No limit.
The number of seats may be reduced in the late registration period.
- Study Board of Mathematics and Computer Science
- Department of Mathematical Sciences
- Faculty of Science
- Jostein Paulsen (7-6c717576676b70426f63766a306d7730666d)
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Courseinformation of students