Course - Spatial Statistics - TMA4250
Spatial Statistics
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About the course
Course content
Statistical models for spatially varying phenomena, and statistical methods to learn about such phenomena based on data points with spatial coordinates. The most common example of "space" is a geographical area. More specifically, the course contains model specification, simulation, prediction, and parameter estimation, for continuously- and discretely-indexed random fields, and for point processes. Specifically for Gaussian, Poisson and Markov random fields. Examples from ecology, epidemiology and geophysics.
Learning outcome
1.Knowledge. The student has knowledge about basic concepts of the theory about Gaussian random fields, including algorithms for unconditional and conditional simulation, and spatial prediction by various types of kriging. The student also has knowledge about basic concepts of the theory of point processes, spatial Poisson and Cox random fields, and MCMC-algorithms for simulation of such point processes. Moreover, the student has knowledge about basic concepts of the theory of Markov random fields, including concepts as cliques, neighborhoods and potential function and insight into the Hammersley-Clifford theorem. Further, knowledge of simulation of Markov random fields by use of MCMC algorithms. Lastly, the student has knowledge of the basic theory of parameter estimation in spatial random fields.
2. Skills. The student can formulate statistical models for simple spatial phenomena, and perform parameter estimation under these models by use of suitable computer software. Moreover, the student can evaluate conditional models by stochastic simulation and perform spatial prediction.
Learning methods and activities
Lectures and compulsory work (projects).
Compulsory assignments
- Work
Further on evaluation
An aggregate score is given in the course. This aggregate score comprises a written final examination (70%) and a portfolio assessment (30%). The results are given by the letter grading system.
The portfolio comprises three written reports that are submitted during the semester, and one final revised written report submitted no later than the day of the written exam. Feedback is given for each of the first three written reports, and the student chooses to revise one of these. All written reports must be submitted, but only the revised written report forms the basis for the evaluation of the portfolio.
Students are free to choose Norwegian or English for written assessments.
Retake of the written final examination may be given as an oral examination. The retake exam is in August.
When retaking a passed exam, both the written final examination and the portfolio assessment must be retaken.
Recommended previous knowledge
TMA4240/TMA4245 Statistics and TMA4300 Computer Intensive Statistical Methods (may be taken as a parallel), or equivalent. The subject requires a mature understanding of statistics and we also recommend TMA4265 Stochastic Processes and TMA4267 Linear Statistical Models.
Course materials
Will be announced at the start of the course.
Credit reductions
| Course code | Reduction | From |
|---|---|---|
| SIF5064 | 7.5 sp |
Other pages about the course
Subject areas
- Statistics
- Technological subjects