Course syllabus

Spatial statistik med bildanalys
Spatial Statistics with Image Analysis

FMSN20, 7,5 credits, A (Second Cycle)

Valid for: 2012/13
Decided by: Education Board 1
Date of Decision: 2012-03-27

General Information

Elective for: C5, D5, D5-bg, E5, E5-mt, E5-bg, F5, F5-mt, F5-bg, Pi4, Pi4-mrk, Pi4-ssr
Language of instruction: The course will be given in English on demand


The aim of the course is to provide the student with tools for handling high-dimensional statistical problems. The course contains models, and methods with practical applications, mainly for spatial statistics and image analysis. Of special importance are the Bayesian aspects, since they form the foundation for many modern spatial statistical and image analysis methods. The course emphasises methods with appications in climate, environmental statistics, and remote sensing.

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


Bayesian methods for stochastic modelling, classification and reconstruction. Random fields, Gaussian random fields, Kriging, Markov fields, Gaussian Markov random fields, non-Gaussian observationer. Covariance functions, multivariate techniques. Simulation methods for stochastic inference (MCMC, etc.). Applications in climate, environmental statistics, remote sensing, and spatial statistics.

Examination details

Grading scale: TH
Assessment: Written and oral project presentation.


Admission requirements:

Required prior knowledge: A course in Image analysis or at least one course in Markov processes or Stationary stochastic processes. Matlab proficiency.
The number of participants is limited to: No
The course might be cancelled: If the number of applicants is less than 16.
The course overlaps following course/s: FMS150, MAS228, MASM13

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 MASM25.