Computational Inference in Dynamical Systems

A PhD course at Vrije Universiteit Brussel.

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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.

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

Lecture slides are available here.

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 course is part of the PhD course Identification of Nonlinear Dynamic Systems

Date and Time

This is an intensive course (4*2 hours of lecturing) and it will be given in two parts,

• June 6, 2012 (8-12)
• June 7, 2012 (8-12)

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

Identification of Nonlinear Dynamic Systems, System identification, Machine learning.

Contact Persons

Dr Thomas Schön, tel +46 13 281373, email: schon_at_isy.liu.se.
Prof. Johan Schoukens, tel +32 (0)2 629 29 44, email: Johan.Schoukens_at_vub.ac.be.