Computational Inference in Dynamical Systems

A PhD course at KTH, Stockholm, Sweden.

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General Information

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.

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Links

A pdf document with a more detailed course description is available here.

Detailed schedule and lecture slides are available here.

Information about the implementation projects is available here.

News related to the course are available here.

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Contents

• Probabilistic modeling of dynamical systems
• Expectation maximisation (EM) for nonlinear system identification
• Markov chain Monte Carlo methods (MCMC) for nonlinear system identification
• Gibbs sampler
• Metropolis hastings sampler
• Rejection sampling and Importance sampling
• Particle filtering
• Particle smoothing
• Particle MCMC

Organization

This is an intensive course (10 hours of lecturing during 4 days)

• November 19, 2012 (3h)
• November 20, 2012 (2h)
• November 22, 2012 (3h)
• November 23, 2012 (2h)

A detailed schedule is available here.

Course Literature

Lecture notes will be handed out to the course participants,
[LN] Thomas B. Schön. Computational inference and learning in dynamical systems, 2012.

Prerequisites

Basic undergraduate courses in linear algebra, statistics, signal and systems.

Related Courses

Machine learning, System identification.

Contact Persons

Dr Thomas Schön, tel +46 (0) 13 281373, email: schon_at_isy.liu.se.
Dr. Cristian Rojas, tel +46 (0) 8 790 7427, email: cristian.rojas_at_ee.kth.se.
Prof. Håkan Hjalmarsson, tel +46 (0)8 790 8464, email: hakan.hjalmarsson_at_ee.kth.se.