Course syllabus

Stationära stokastiska processer
Stationary Stochastic Processes

FMSF10, 7,5 credits, G2 (First Cycle)

Valid for: 2023/24
Faculty: Faculty of Engineering, LTH
Decided by: PLED I
Date of Decision: 2023-04-14

General Information

Compulsory for: Pi3
Elective Compulsory for: MWIR1
Elective for: BME4-sbh, C4-ks, D4-bg, D4-ssr, D4-mai, E4-ss, E4-bg, F4, F4-bg, F4-bm, F4-fm, F4-r, F4-ss, F4-mai, I4-fir, M4, MMSR2, R4
Language of instruction: The course will be given in English


The student shall aquire a toolbox containing concepts and models for description and handling of stationary stochastic processes within many different areas, such as, signal processing, automatic control, information theory, economics, biology, chemistry, and medicine. The mathematical and statistical elements are therefore illustrated using a wide variety of examples from different areas of application.

The course shall also give the student the ability to identify the presence of stationary processes in other courses in the education, use the knowledge of stationary processes in other courses, and translate the concepts and tools between different courses, building on stationary processes.

Learning outcomes

Knowledge and understanding
For a passing grade the student must

Competences and skills
For a passing grade the student must

Judgement and approach
For a passing grade the student must


Examination details

Grading scale: TH - (U,3,4,5) - (Fail, Three, Four, Five)
Assessment: Written exam and compulsory computer exercises.

The examiner, in consultation with Disability Support Services, may deviate from the regular form of examination in order to provide a permanently disabled student with a form of examination equivalent to that of a student without a disability.

Code: 0115. Name: Examination.
Credits: 6. Grading scale: TH. Assessment: Written examination.
Code: 0215. Name: Laboratory part 1.
Credits: 0,5. Grading scale: UG. Assessment: The first computer exercise
Code: 0315. Name: Laboratory part 2.
Credits: 1. Grading scale: UG. Assessment: The rest of the computer exercises


Admission requirements:

Assumed prior knowledge: A basic course in mathematical statistics and knowledge in complex and linear analysis.
The number of participants is limited to: No
The course overlaps following course/s: FMS045, FMS047, MASC04, MASC14

Reading list

Contact and other information

Director of studies: Johan Lindström,
Course administrator: Susann Nordqvist,
Course homepage:
Further information: Also given at the faculty of science with the course code MASC14.