course-details-portlet

TMA4250 - Spatial Statistics

About

Examination arrangement

Examination arrangement: Aggregate score
Grade: Letter grades

Evaluation Weighting Duration Grade deviation Examination aids
School exam 70/100 4 hours C
Portfolio 30/100

Course content

Model specification, simulation and prediction, moreover parameter estimation, in continuous, event and mosaic random fields. Specifically in 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, spatial prediction by various types of kriging. The student also has knowledge about basic concepts of the theory of event random fields, spatial Poisson and Cox random fields, and MCMC-algorithms for simulation of such event fields. Moreover, the student has knowledge about basic concepts of the theory of Markov random fields, including consepts 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). The lectures may be given in English.

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 for the constituent parts 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 one of these written reports to revise. All written reports must be submitted, but only the revised written report forms the basis for the evaluation of the portfolio.

If the course is taught in English, the written final exam may be given only in English. Students are free to choose Norwegian or English for written assessments.

Retake of the written final examination may be given as an oral examination. When retaking a passed exam, both the written final examination and the portfolio assessment must be retaken.

Specific conditions

Compulsory activities from previous semester may be approved by the department.

Course materials

Will be announced at the start of the course.

Credit reductions

Course code Reduction From To
SIF5064 7.5
Facts

Version: 1
Credits:  7.5 SP
Study level: Second degree level

Coursework

Term no.: 1
Teaching semester:  SPRING 2023

Language of instruction: English, Norwegian

Location: Trondheim

Subject area(s)
  • Statistics
  • Technological subjects
Contact information
Course coordinator:

Department with academic responsibility
Department of Mathematical Sciences

Examination

Examination arrangement: Aggregate score

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Spring ORD School exam 70/100 C INSPERA
Room Building Number of candidates
Spring ORD Portfolio 30/100 INSPERA
Room Building Number of candidates
Summer UTS School exam 70/100 C INSPERA
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
Examination

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

More on examinations at NTNU