Course syllabus

Monte Carlo-baserade statistiska metoder
Monte Carlo and Empirical Methods for Stochastic Inference

FMS091, 7,5 credits, A (Second Cycle)

Valid for: 2016/17
Decided by: Education Board B
Date of Decision: 2016-03-28

General Information

Elective for: BME4, D4, F4, F4-bm, F4-fm, I4, I4-fir, Pi4-bs, Pi4-fm, Pi4-bg
Language of instruction: The course will be given in English on demand


The purpose of the course is to give the students tools and knowledge to handle complex statistical problems and models. The aim is that students shall gain proficiency with modern computer intencive statistical methods and use these to estimate quantities and parameters in complex models that arise in different applications (e.g. economics, signal processing, biology, climate, and environmental statistics). Further, the student should be able to assess the uncertainty of these estimates. The main aim lies in enhancing the scope of statistical problems that the student will be able to solve.

Learning outcomes

Knowledge and understanding
For a passing grade the student must

Competences and skills
For a passing grade the student must

Judgement and approach
For a passing grade the student must


Simulation based methods of integration and statistical analysis. Monte Carlo methods for sequential problems. Markov chain methods, e.g. Gibbs sampling and the Metropolis-Hastings algorithm, for simulation and inference. Bayesian modelling and inference. The re-sampling principle, both non-parametric and parametric. Methods for constructing confidence intervals using re-sampling. Simulation based tests as an alternative to asymptotic parametric tests.

Examination details

Grading scale: TH
Assessment: Written and oral project presentation. The final grade is given by a summary of the results of the different parts of the examination.

Code: 0116. Name: Project Part 1.
Credits: 2,5. Grading scale: UG. Assessment: Written report on the first project
Code: 0216. Name: Project Part 2.
Credits: 5. Grading scale: UG. Assessment: Written and oral report on the rest of the projects


Admission requirements:

Required prior knowledge: Programming experience. FMSF05 Probability theory helps.
The number of participants is limited to: No
The course overlaps following course/s: MASM11

Reading list

Contact and other information

Director of studies: Studierektor Anna Lindgren,
Course homepage:
Further information: The course is also given at the faculty of science with the code MASM11.