Computational Inference in Dynamical Systems
The aim of this course is to provide an introduction to the theory and application of computational methods for inference in dynamical systems. More specifically, the computational methods we are referring to are sequential Monte Carlo (SMC) methods (particle filters and particle smoothers) for nonlinear state inference problems and expectation maximisation (EM) and Markov chain Monte Carlo (MCMC) methods for nonlinear system identification.
Links to details
This course is currently under development and as part of this development process I offer the course at various universities around the world. Links to the various editions are available here:
- KTH, Stockholm, Sweden, November 2012, home page.
- USYD, Sydney, Australia, October 2012, home page.
- VUB, Brussels, Belgium, June 2012, home page.
Associate Professor in Automatic Control
- +46 13 281373
- Mobile (private):
- +46 735 933 887
- Dept. of Electrical Engineering
- Linköping University
- SE-581 83 Linköping
- Visiting Address:
- Campus Valla
- Building B
- Room 2A:NNN (in the A corridor on the ground floor between entrance 25 and 27)
Informationsansvarig: Thomas Schön
Senast uppdaterad: 2012-10-13