Schedule

1. Introduction
   • The basic problem
   • Some examples
   • Model selection using ranking
   • Some pitfalls
2. Probabilistic models
   • Models and model structures
   • Estimators
   • A probabilistic toolshed
3. Estimation theory and unbiased parameter estimation
   • Estimation theory
     • Information contents in random variables
     • Estimation of random variables
     • Unbiased parameter estimation
   • Unbiased parameter estimation
     • The Cramér-Rao lower bound
     • Efficient estimators
     • The maximum likelihood estimator
     • Data compression
     • Uniform minimum variance unbiased estimators
     • Best linear unbiased estimator (BLUE)
     • Using estimation for parameter estimation
4. Biased parameter estimation
   • The bias-variance trade-off
   • The Cramér-Rao lower bound
   • Average risk minimization
   • Minimax estimation
   • Pointwise risk minimization
5. Asymptotic theory
   • Limits of random variables
   • Large sample properties of estimators
   • Using estimation for parameter estimation, part II
   • Large sample properties of biased estimators
6. Computational aspects
7. Case studies I
   • Parametric LTI models
   • Impulse response models
8. Case studies II
   • Uncertain input models
   • Nonlinear stochastic state-space models
9. Model accuracy
10. Model structure selection 
11. Experiment design
12. Continuous time identification