Prerequisite/Recommended prerequisite for
participation in the module
The module builds on knowledge obtained by the module Statistical
Inference for Linear Models from the Bachelor of Science (BSc) in
Engineering (Mathematical Engineering)
Content, progress and pedagogy of the
module
The course deals with Markov chain Monte Carlo methods as well
as one or more of the three main topics within spatial
statistics.
Learning objectives
Knowledge
- know the fundamental models and methods within the chosen main
topics (geostatistics, lattice processes or spatial point
processes) as well as Markov chain Monte Carlo.
- have knowledge about the following subjects within the chosen
main topic(s)
- Geostatistics:
Theory for second order stationary processes,
variograms/covariograms, prediction and kriging, as well as model
based geostatistics - Lattice processes:
Markov fields, Brook's factorisation and
Hammersley-Clifford's theorem and likelihood based statistical
analysis - Spatial point processes:
Poisson processes, Cox processes and Markov point processes, as
well as statistical analyses based on non-parametric methods
(summary statistics) and likelihood based methods - Markov chain Monte Carlo:
Fundamental theory of Markov chains with a view to simulation,
Markov chain Monte Carlo methods for simulation of distributions,
including the Metropolis-Hastings algorithm and the Gibbs
sampler
Skills
- are able to explain the main theoretical results from the
course
- are able to perform statistical analyses of concrete
datasets
- are able to simulate the examined models
Competences
- are able to interpret a spatial statistical model in relation
to a concrete dataset and give an account of the limitations of the
model with respect to describing the variation in the dataset using
the theoretical results within spatial statistics
- are able to simulate distributions using Markov chain Monte
Carlo methods and evaluate the output of the Markov chain
Type of instruction
As described in §17.
Extent and expected workload
This is a 5 ECTS course module and the work load is
expected to be 150 hours for the student.
Exam
Prerequisite for enrollment for the exam
- For students on the master level: In order to participate in
the exam, students must have actively participated in course
progress by way of one or several independent oral and/or written
contributions.
Exams
Name of exam | Spatial Statistics and Markov Chain Monte Carlo
Methods |
Type of exam | Written or oral exam
Individual oral or written exam, or individual ongoing
evaluation. |
ECTS | 5 |
Assessment | Passed/Not Passed |
Type of grading | Internal examination |
Criteria of assessment | The criteria of assessment are stated in the Examination
Policies and Procedures |