course-details-portlet

TMA4250 - Spatial Statistics

About

Examination arrangement

Examination arrangement: Portfolio assessment
Grade: Letters

Evaluation form Weighting Duration Examination aids Grade deviation
work 30/100
Written examination 70/100 4 hours C

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 image analysis, and environmental and natural resource applications.

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 has also knowledge about basic
concepts of the theory of event random fields, including K- and L-statistics, spatial Poisson, Cox and Strauss 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 works (projects). Portfolio assessment is the basis for the grade awarded in the course. This portfolio comprises a written final examination (70%) and works (projects) (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. The lectures may be given in English. If the course is taught in English, the exam may be given only in English. Students are free to choose Norwegian or English for written assessments.

Compulsory assignments

  • Work

Further on evaluation

In the case that the student receives an F/Fail as a final grade after both ordinary and re-sit exam, then the student must retake the course in its entirety. Submitted work that counts towards the final grade will also have to be retaken. For more information about grading and evaluation. see «Teaching methods and activities».

Specific conditions

Exam registration requires that class registration is approved in the same semester. 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
More on the course
Facts

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

Coursework

Term no.: 1
Teaching semester:  SPRING 2021

No.of lecture hours: 3
Lab hours: 2
No.of specialization hours: 7

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

Phone:

Examination

Examination arrangement: Portfolio assessment

Term Status code Evaluation form Weighting Examination aids Date Time Digital exam Room *
Spring ORD work 30/100
Room Building Number of candidates
Spring ORD Written examination 70/100 C
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"

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