This page contains the program of the course. Other information, such as learning outcomes, teachers, literature and examination, can be found in a separate course PM.

Program

Literature

We have the following books and acronyms:

• MvL: Theory of Spatial Statistics: A Concise Introduction, M.N.M. van Lieshout (found here)
• BC: Foundations of Computational Imaging: A Model-Based Approach, C. A. Bouman (found here)
• LN: Lecture notes Statistics of Imaging, M. Rudemo (found here)

Other relevant literature (which might be used in the course):

• HS: Handbook of spatial statistics, Gelfand, Diggle, Guttorp & Fuentes (found here)
• EL: The Elements of Statistical Learning, Hastie, Tibshirani & Friedman (found here)
• GH: Image Analysis for the Biological Sciences, Glasbey & Horgan (found here)

Note that all literature is freely available via the Chalmers library for registered students.

 

If you want to recap some basic probability and statistics notions, you may e.g. consult Section 2 and 3 of the book by Bouman (BC).

 

Examination and projects

The examination consists of two parts: 

• A group-based project assignment. Grade: Pass/Fail.
• A written exam on June 3 2026, 14:00-18:00. Allowed exam aids: Chalmers approved calculator. Final grade (Chalmers: U-3-4-5; GU: U-G-VG): 3/G: 20 p, 4: 30 p , 5: 40 p, VG: 37.5 p, max: 50 p.

To pass the course, a student has to pass both the project part and the written exam. The final grade (Chalmers: U-3-4-5; GU: U-G-VG) on the course is based on the exam, which is individual.

The deadlines for the projects can be found at the end of this page under Course summary. All assignments should be uploaded under Canvas/Assignments.  

The exact dates, times and places for the (re)exams can be found on the student portal.

 

Project details:

The project work is done in groups of 1-3 students - please create a project group as early as possible (go to People and then to Project groups to find the groups). Once you have decided who to be in a group with, make sure that each group member goes to People and then to Project groups to register into the appropriate group slot. If you can not find anybody to be in a group with, please reach out to other people who you see have not yet been registered to a group or to people who are in a group which you see is not yet full - if you are unsuccessful in this endeavour, please reach out to Ottmar and Mathis as soon as possible so that they can help you to find a group to join.

The project involves that the group has to i) hand in a project report and ii) given an oral presentation of the project.  The project has got a slightly new focus compared to previous years.

Each group selects any paper from the journal Spatial Statistics, which you can access via https://www.sciencedirect.com/journal/spatial-statistics (once you are logged in to the Chalmers library). Once you have reached the journal homepage, make searches based on different keywords which interest you (as a group), e.g. geostatistics, random field, point pattern, point process, Gibbs/Markov random field/state, disease mapping, image analysis, etc.

• The choice of paper has to be reported as a Canvas assignment (intended as a check from the course instructors). The deadline for this is deliberately set to after we have covered all the chapters 1-4 in van Lieshout's book.
• The group then reads the selected paper in detail and writes a summary/report on it, which needs to be handed in as a Canvas assignment towards the end of the course (the exact deadline will be found under the appropriate Canvas assignment).
• Each group must also give an oral presentation of its selected paper summary/report; the oral presentation should be planned for 15 minutes. After the presentation, each group gets asked questions for approximately 5 minutes; the course instructors will direct individual questions to the group members.

In summary, the workflow for a student to pass this part of the course, i.e. the project, is that the student: 1) reads and understands the group’s paper, 2) takes part in writing the report, 3) has the group submit the report through Canvas, 4) takes part in putting together and preparing the group’s oral presentation, 5) carries out the oral presentation together with the group members, 6) answers questions directed to the student.

 

Lectures and Exercises

The schedule of the course is in TimeEdit.

The course typically has two lectures and two computer exercises each week. Details for these are given in the schedule below, which will be updated during the course. For each lecture, the chapters covered in the books will be listed.

The lectures will be given in Euler.

Teachers of the course are Ottmar Cronie (lectures, examiner), ottmar@chalmers.se, and Mathis Rost (lectures, exercises/labs), mathisr@chalmers.se. Lecture notes of the lectures can be found in Files/Lecture notes.

The information below will be updated and extended on an ongoing basis.

 

Date	Weekday	Time	Room		Teacher	Content
Week 13
March 23	Monday	8:00-9:45	Euler	L1	Ottmar	Introduction MvL: Section 1, 2.1 Lecture notes: L1.pdf
	Monday	13:15-15:00	MVF24 and MVF25	E1	Ottmar	Exercise sheet 1: E1.pdf Matlab files: TMS016_Lab1B.zip   Exercise sheet solution: Lab1_solutions.pdf Matlab solution:TMS016_Lab1_solution.zip
March 25	Wednesday	10:00-11:45	Euler	L2	Ottmar	Gaussian random fields, different notions of stationarity and isotropy. MvL: Section 2.2, 2.3 LN: Section 5.1, 5.2 Lecture notes: L2.pdf
	Wednesday	13:15-15:00	Online (MVF24 and MVF25)	E2	Mathis	Link for Zoom:  TMS016 / MSA301 Spatial statistics and image analysis LAB2 Time: Mar 25, 2026 01:15 PM Stockholm Join from PC, Mac, Linux, iOS or Android: https://chalmers.zoom.us/j/63497363318     Password: 393786     Meeting ID: 634 9736 3318       Exercise sheet solution: Lab1_solutions.pdf Matlab solution:TMS016_Lab1_solution.zip
Week 14
March 30	Monday	8:00-9:45	Euler	L3	Ottmar	Construction of covariance functions, intrinsic stationarity, and (semi) variograms and their modelling. MvL: Section 2.3, 2.4, 2.6 LN: Section 5.2, 5.3 Lecture notes: L3.pdf
	Monday	13:15-15:00	MVF24 and MVF25	E3	Cancelled due to illness	Exercise sheet 2: E2.pdf Exercise sheet solution partA: E2_solution.pdf Exercise sheet solution partB:lab2_solution.R
April 1	Wednesday	10:00-11:45	Euler	L4	Ottmar	Kriging  MvL: Section 2.7, 2.8, 2.9, 2.10, 2.11 LN: Section 5.4 Lecture notes: L4.pdf
	Wednesday	13:15-15:00	MVF24 and MVF25	E4	Mathis
Week 15					Break
Week 16
April 13	Monday	8:00-9:45	Euler	L5	Ottmar	Areal unit data, discrete random fields, neighbourhood relations, local characteristics, Ising model, Besag's theorem, conditional autoregrssion (CAR) models, conditional independence.  MvL: Section 3.1, 3.2 LN: Section 4 Lecture notes: L5.pdf
	Monday	13:15-15:00	MVF24 and MVF25	E5	Mathis	Exercise sheet 3: E3.pdf
April 15	Wednesday	10:00-11:45	Euler	L6	Ottmar	Gibbs states, (normalised) interaction potentials, partition functions, cliques, Markov random fields, Hammersely-Clifford theorem, Gaussian Markov random fields. MvL: Section 3.3, 3.4 LN: Section 4 BC: Section 4, 6.1, 6.2, 14.1-14.4 Lecture notes: L6.pdf
	Wednesday	13:15-15:00	MVF24 and MVF25	E6	Mathis
Week 17
April 20	Monday	8:00-9:45	Euler	L7	Ottmar	Statistical inference for discrete random fields, (Monte-Carlo) maximum likelihood estimation, pseudolikelihood estimation, MCMC simulation (Gibbs sampling and Metropolis-Hastings sampling). MvL: Section 3.5, 3.6 BC: Section 14.5, 15 Lecture notes: L7.pdf
	Monday	13:15-15:00	MVF24 and MVF25	E7	Mathis
April 22	Wednesday	10:00-11:45	Online lecture (via Zoom) so no class attendance! Zoom-link: https://chalmers.zoom.us/j/63704755201 Password: 359651	L8	Ottmar	Hierarchical modelling, disease mapping, image segmentation, posterior inference and MAP estimation. MvL: Section 3.7 We only mention what MvL says about MAP and segmentation here but more details can be found in Bouman's book (BC: Section 2.3, 5.1, 7, 16.1) Lecture notes: L7.pdf + Guest lecture: Adinia Iftimi, University of Valencia, Spain. Recording:
	Wednesday	13:15-15:00	MVF24 and MVF25	E8
Week 18
April 27	Monday	8:00-9:45	Euler	L9		MvL: Section 4
	Monday	13:15-15:00	MVF24 and MVF25	E9
April 29	Wednesday	10:00-11:45	Euler	L10		MvL: Section 4
	Wednesday	13:15-15:00	MVF24 and MVF25	E10
Week 19
May 4	Monday	8:00-9:45	Euler	L11		MvL: Section 4
	Monday	13:15-15:00	MVF24 and MVF25	E11
May 6	Wednesday	10:00-11:45	Euler	L12
	Wednesday	13:15-15:00	MVF24 and MVF25	E12
Week 20
May 11	Monday	8:00-9:45	Euler	L13
	Monday	13:15-15:00	MVF24 and MVF25	E13
May 13	Wednesday	10:00-11:45	Euler	L14
	Wednesday	13:15-15:00	MVH12	E14
Week 21
May 18	Monday	8:00-9:45	Euler	L15
	Monday	13:15-15:00	MVH12	E15
May 20	Wednesday	10:00-11:45	Euler	L16
	Wednesday	13:15-15:00	MVH12	E16

 

Back to the top