Schedule

Preliminary schedule

1. Introduction (Friday 15/5, 15-17)  
   • The basic problem
   • Some examples
   • Model selection using ranking
   • Some pitfalls
2. Probabilistic models (Tuesday 19/5, 10-12) 
   • Models and model structures
   • Estimators
   • A probabilistic toolshed
3. Estimation theory and Wold decomposition (Tuesday 26/5, 10-12) 
   • Estimation theory
     • Information contents in random variables
     • Estimation of random variables
   • Wold decomposition
4. Unbiased parameter estimation (Friday 29/5, 15-17) 
   • 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
5. Biased parameter estimation (Tuesday 2/6, 10-12) 
   • The bias-variance trade-off
   • The Cramér-Rao lower bound
   • Average risk minimization
   • Minimax estimation
   • Pointwise risk minimization
6. Asymptotic theory (Friday 5/6, 15-17) 
   • Limits of random variables
   • Large sample properties of estimators
   • Using estimation for parameter estimation, part II
   • Large sample properties of biased estimators
7. Computational aspects (Tuesday 9/6, 08-10) 
   • Gradient based optimization
   • Convex relaxations
   • Integration by Markov Chain Monte Carlo (MCMC) methods
8. Case studies I (Friday 12/6, 10-12) 
   • Parametric LTI models
   • Impulse response models
9. Case studies II (Tuesday 16/6, 10-12) 
   • Uncertain input models
   • Nonlinear stochastic state-space models
10. Model accuracy (Friday 19/6, 15-17) 
11. Model structure selection and model validation  (Tuesday 23/6, 10-12) 
12. Experiment design (Tuesday 25/8, 10-12) 
13. Continuous time identification (Friday 28/8, 15-17)