FEM3210 Estimeringsteori 10,0 hp

·        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

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.

Sufficiency in probability theory, calculus and linear algebra (matrix analysis useful but not required).

• EXA1 - Examination, 10,0 hp, betygsskala: P, F

Examinator beslutar, baserat på rekommendation från KTH:s handläggare av stöd till studenter med funktionsnedsättning, om eventuell anpassad examination för studenter med dokumenterad, varaktig funktionsnedsättning.

Examinator får medge annan examinationsform vid omexamination av enstaka studenter.

När kurs inte längre ges har student möjlighet att examineras under ytterligare två läsår.

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.

Magnus Jansson

• Vid grupparbete har alla i gruppen ansvar för gruppens arbete.
• Vid examination ska varje student ärligt redovisa hjälp som erhållits och källor som använts.
• Vid muntlig examination ska varje student kunna redogöra för hela uppgiften och hela lösningen.