1. Course title
Statistisk inversion metoder
Statistical Inverse Methods
2. Course code
Previous codes: 53834 Tilastolliset inversiomenetelmät, 5 cr
3. Course status: compulsory
-Which degree programme is responsible for the course?
Master’s Programme in Particle Physics and Astrophysical Sciences
-Which module does the course belong to?
PAP300 Advanced Studies in Particle Physics and Astrophysical Sciences (compulsory for Study Track in Astrophysical Sciences)
ATM300 Advanced Studies in Atmospheric Sciences (optional for Study Track in Meteorology)
-Is the course available to students from other degree programmes?
4. Course level (first-, second-, third-cycle/EQF levels 6, 7 and 8)
Master’s level, degree programmes in medicine, dentistry and veterinary medicine = secondcycle
degree/EQF level 7
Doctoral level = third-cycle (doctoral) degree/EQF level 8
-Does the course belong to basic, intermediate or advanced studies (cf. Government Decree
on University Degrees)?
5. Recommended time/stage of studies for completion
Recommended time for completion is in the mid-phase of Master's studies.
6. Term/teaching period when the course will be offered
Annually in the spring term, periods 3–4.
7. Scope of the course in credits
8. Teacher coordinating the course
University researcher Antti Penttilä.
9. Course learning outcomes
You will learn
- Advanced statistical methods to describe and analyze research data
- Theory and practice of statistical estimation and testing
- Multivariate methods
- Monte Carlo statistical techniques
- Bayesian inference
- Statistical inversion using Markov Chain Monte Carlo methods
10. Course completion methods
The student must complete weekly excercise tasks, which will include traditional 'pen-and-paper' problems and computer tasks. There will be final exam in the end of the cource.
MAPU I–III–III, Scientific Computing I–III, Havaintojen tilastollinen käsittely (New course codes will be updated when available)
12. Recommended optional studies
-Scientif Computing II
13. Course content
Statistical inference, linear model, nonlinear model, kernel estimation, multivariate methods, Bayesian inference, Monte Carlo methods, MCMC.
14. Recommended and required literature
The material is collected to handout that is distributed to students.
15. Activities and teaching methods in support of learning
Weekly lectures and exercises.
16. Assessment practices and criteria, grading scale
Final grade is based on exercises and final exam.
17. Teaching language