Course content

Inference, simulation and applications of Gaussian, point and Markov random fields. Examples from image analysis and environment and natural resource applications.

Learning outcome

The course provides an introduction to statistical methodology relevant for analysis of spatial phenomena. The students should be able to make stochastic models and do inference of simple spatial phenomena.

Learning methods and activities

Lectures and work with a project/term paper. The exercises demand the use of a computer. The lectures may be given in English. Portfolio assessment is the basis for the grade awarded in the course. This portfolio comprises a written final examination 70% and the semester assignment 30%. The results for the constituent parts are to be given in %-points, while the grade for the whole portfolio (course grade) is given by the letter grading system. Retake of examination may be given as an oral examination.

Compulsory assignments

• Exercises

Recommended previous knowledge

One of the courses TMA4240/TMA4245 Statistics, and TMA4300 Computer intensive statistical methods (may be taken as a parallel), or equivalent. The subject require a mature understanding of statistics and we also recomend TMA4265 Stochastic Processes and TMA4267 Linear statistical models.

Course materials

Will be announced at the start of the course.

Credit reductions