Parameter Estimation 2020 Spring

Parameter Estimation, 2020 Spring semester

Course data

Lecturer: Dr. Gábor Szederkényi

Teaching assistant:  Gergely Szlobodnyik

Lectures: Wednesday 14-16,

Labs: website

Planned schedule (updated for online education):  Parameter_estimation_schedule_updated_2020

Requirements (updated for online education): PE_requirements_online_updated_2020

Lecture slides:

Lecture 1: Introduction – 01_Introduction_2020

Lecture 2: Math review, linear regression – 02_Math_rev_regression_2020

Lecture 3: Least squares method for predictive models – 03_Least_squares_Predictive_2020

Lecture 4: ML estimate, Cramer-Rao Bound – 04_ML_CramerRao_2020

Lecture 5: Recursive estimation –  05_Recursive_2020

Lecture 6: Instrumental variable method – 06_InstrumentalVariable_2020

Lecture 7: Bayesian estimation – 07_Bayes_2020

Supplementary material for Lecture 7: V_Peterka-Bayesian_Identification

Lecture 8: Optimization-based estimation – 08_NonlOptim_2020

Lecture 9: Illustrative examples, some related results (optional supplementary material, not compulsory) – 09_Illustrative

Lecture 10: Latent variable models – 10_LatentVariableModels

Lecture 11: Identifiability – 11_Identifiability


Suggested literature:

  • L. Jung: System Identification – Theory for the User, Prentice Hall, 1987
  • E. Walter and L. Pronzato: Identification of Parametric Models from Experimental Data, Springer, 1997

Additional example (in Hungarian):  Italy_report_Szederkenyi

Possibility of Teams presentations at examsExam_Project_Teams