Schedule

* IntroductionPreliminary schedule¶


* Introduction (Friday 15/5, 15-17)
* The basic problem
* Some examples
* Model selection using ranking
* Some pitfalls

* Probabilistic models (Tuesday 19/5, 10-12)
* Models and model structures
* Estimators
* A probabilistic toolshed

* Estimation theory and unbiased parameter estimationWold decomposition (Tuesday 26/5, 10-12)
* Estimation theory
* Information contents in random variables
* Estimation of random variables
* Unbiased parameter estimation

* Wold decomposition

* 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


* 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

* 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

* Computational aspects
* Case studies I (Tuesday 9/6, 08-10)
* Gradient based optimization
* Convex relaxations
* Integration by Markov Chain Monte Carlo (MCMC) methods

* Case studies I (Friday 12/6, 10-12)
* Parametric LTI models
* Impulse response models

* Case studies II (Tuesday 16/6, 10-12)
* Uncertain input models
* Nonlinear stochastic state-space models

* Model accuracy (Friday 19/6, 15-17)
* Model structure selection
* Experiment designand model validation (Tuesday 23/6, 10-12)
* Experiment design (Tuesday 25/8, 10-12)
* Continuous time identification (Friday 28/8, 15-17)