· 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 for research students about course offerings
The course is given every odd year in period 1-2.
Choose semester and course offering
Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.
Application
For course offering
Autumn 2023 Start 28 Aug 2023 programme students
Application code
51191
Headings with content from the Course syllabus FEM3210 (Spring 2019–) 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.
Literature and preparations
Specific prerequisites
Sufficiency in probability theory, calculus and linear algebra (matrix analysis useful but not required).
Recommended prerequisites
Sufficiency in probability theory, calculus and linear algebra (matrix analysis useful but not required).
Equipment
No information inserted
Literature
No information inserted
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
Grading scale
P, F
Examination
- EXA1 - Examination, 10.0 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.
Other requirements for final grade
A score of 90 on 58 homework problems (78%) grades according to; 0: didn't try or completely incorrect, 1: almost correct (or solved parts of the problem), 2: correct. Completion of 2 project assignments. 50% on 48h take home exam.
Opportunity to complete the requirements via supplementary examination
No information inserted
Opportunity to raise an approved grade via renewed examination
No information inserted
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
Main field of study
This course does not belong to any Main field of study.
Education cycle
Third cycle
Add-on studies
No information inserted
Contact
Magnus Jansson (janssonm@kth.se)