- Introduction
- Minimum Variance Unbiased Estimation, Cramer-Rao Lower Bound
- Linear Estimators
- Maximum Likelihood
- Least Squares
- The Method of Moments
- Bayesian Methods
- Extension to Complex Data
FEM3210 Estimation Theory 10.0 credits
Information per course offering
Information for Autumn 2025 Start 25 Aug 2025 programme students
- Course location
KTH Campus
- Duration
- 25 Aug 2025 - 10 Oct 2025
- Periods
- Pace of study
100%
- Application code
10296
- Form of study
Normal Daytime
- Language of instruction
English
- Course memo
- Course memo is not published
- Number of places
Places are not limited
- Target group
- No information inserted
- Planned modular schedule
- No information inserted
- Schedule
- Schedule is not published
- Part of programme
- No information inserted
Contact
Course syllabus as PDF
Please note: all information from the Course syllabus is available on this page in an accessible format.
Course syllabus FEM3210 (Autumn 2025–)Headings with content from the Course syllabus FEM3210 (Autumn 2025–) are denoted with an asterisk ( )
Content and learning outcomes
Course contents
Intended learning outcomes
After the course the student should be able to:
- Describe the difference between the classical and Bayesian approach to estimation; describe the notions of estimator bias, variance, and efficiency; and describe the notion of sufficient statistics and its meaning in minimum variance unbiased (MVU) estimation.
- Formulate system models and parameter estimation problems and derive corresponding Cramer-Rao lower bounds and sufficient statistics. Prove optimality of estimators.
- Apply appropriate estimators – including linear, least squares, maximum likelihood, and method of moments estimators – after considering estimation accuracy and complexity requirements
- Work with both real and complex valued data models.
- Reflect on sustainability and equity aspects as well as ethical issues related to the course content and its use
Literature and preparations
Specific prerequisites
- Knowledge in linear algebra, 7,5 higher education credits, equivalent SF1624/SF1672/SF1684.
- Knowledge in probability and statistics, 6 higher education credits, equivalent SF1910-SF1924/SF1935
Literature
You can find information about course literature either in the course memo for the course offering or in the course room in Canvas.
Examination and completion
Grading scale
P, F
Examination
- PRO1 - Project assignment, 1.5 credits, grading scale: P, F
- INL1 - Homework, 3.5 credits, grading scale: P, F
- SEM1 - Student presentation, 1.5 credits, grading scale: P, F
- PRA1 - Peer grading, 1.5 credits, grading scale: P, F
- TEN1 - Take home exam, 2.0 credits, grading scale: P, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
If the course is discontinued, students may request to be examined during the following two academic years.
Examiner
Ethical approach
- All members of a group are responsible for the group's work.
- In any assessment, every student shall honestly disclose any help received and sources used.
- In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.
Further information
Course room in Canvas
Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.
Offered by
Education cycle
Third cycle
Transitional regulations
Students who have not completed a course under the previous rules can be examined within two years of the new plan coming into effect. The old module EXA1 contains all the new modules together.