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Version skapad av Håkan Hjalmarsson 2020-05-07 08:31
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Schedule
Preliminary 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 Wold decomposition (Tuesday 26/5, 10-12)
- Estimation theory
- Information contents in random variables
- Estimation of random variables
- Unbiased parameter estimation
- Wold decomposition
- Estimation theory
- 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 (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 and model validation (Tuesday 23/6, 10-12)
- Experiment design (Tuesday 25/8, 10-12)
- Continuous time identification (Friday 28/8, 15-17)