The course covers the following fields:

− minimum variance estimation

− Cramér-Rao bound

− best linear unbiased estimation

− maximum likelihood estimation

− least squares method

− the method of moments

− Bayesian estimation

− extensions for complex data and parameters.

After passing the course, the student should be able to:

− explain the difference between classical and Bayesian estimation

− describe concepts such as unbiased estimator, estimator variance and efficiency

− explain the concept of sufficient statistics and its importance for minimum variance estimation

− formulate system models and parameter estimation problems and derive corresponding Cramér-Rao bounds and sufficient statistics

− apply appropriate estimators (including linear, least-squares, maximum likelihood, method of moments, and maximum a posteriori) after taking into account estimation accuracy and complexity

− work with both real and complex valued data

− reflect on sustainability, equity and ethical issues related to the course content and its use.

• Lectures 6 or 7*2 h
• Weekly homework assignments, 6 problem sets
• Assignment on sustainability, equity and ethical issues
• One mandatory project
• Written Exam 

The project assignment requires simulation in Matlab (or Python) and report writing.  

"Fundamentals of Statistical Signal Processing: Estimation Theory," Kay, Steven M. ISBN 0133457117.

The project assignment requires simulation in Matlab (or Python) and report writing.  

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• PROA - Project assignment, 1.5 credits, grading scale: A, B, C, D, E, FX, F
• INLA - Homework, 3.5 credits, grading scale: A, B, C, D, E, FX, F
• TENA - Written exam, 1.0 credits, grading scale: A, B, C, D, E, FX, 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.

To pass the course you need to pass all examination modules. The final grade will be a weighted average of the grades from the three modules. Preliminary 40/40/20% on Exam/HW/Project.

Preliminary grading on the home assignments will be: E=60%, D=65% , C=70%, B=80%, A=90% of max score.

• 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.

The official course syllabus is valid from autumn semester 2025 according to the decision of Director of First and Second Cycle Education: HS-2025-0537.

Date of decision: 2025-03-31