Course syllabus

Spatial statistik med bildanalys
Spatial Statistics with Image Analysis

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

Valid for: 2023/24
Faculty: Faculty of Engineering, LTH
Decided by: PLED I
Date of Decision: 2023-04-14

General Information

Elective for: BME4, C4, D5-bg, E4-bg, F4, F4-bg, Pi4-ssr, Pi4-biek, Pi4-bam, MMSR2, R4
Language of instruction: The course will be given in English


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 (Gibbs sampling). Applications in climate, environmental statistics, remote sensing, and spatial statistics.

Examination details

Grading scale: TH - (U,3,4,5) - (Fail, Three, Four, Five)
Assessment: Written and oral project presentation. The final grade is determined by the result of the project parts.

The examiner, in consultation with Disability Support Services, may deviate from the regular form of examination in order to provide a permanently disabled student with a form of examination equivalent to that of a student without a disability.

Code: 0115. Name: Project Part 1.
Credits: 2,5. Grading scale: UG. Assessment: Written project report
Code: 0215. Name: Project Part 2.
Credits: 5. Grading scale: UG. Assessment: Written and oral project presentation


Admission requirements:

Assumed prior knowledge: At least one course in Markov processes or Stationary stochastic processes. Matlab proficiency.
The number of participants is limited to: No
The course overlaps following course/s: FMS150, MASM13, MASM25

Reading list

Contact and other information

Director of studies: Johan Lindström,
Course administrator: Susann Nordqvist,
Course homepage:
Further information: In addition to the Handbook of Spatial Statistics other literature, freely available from the Lund University Library, might be recommended. The course is also given at the faculty of science with the code MASM25.