Sequential Monte Carlo methods 2019
The aim of this course is to provide an introduction to the theory and application of sequential Monte Carlo (SMC) methods. To this end we will start by studying the use of SMC for inference in nonlinear dynamical systems. It will be shown how SMC can be used to solve challenging parameter (system identification) and state inference problems in nonlinear dynamical systems. Importantly, we will also discuss SMC in a more general context, showing how it can be used as a generic tool for sampling from complex probability distributions.
This is an intensive 1 week course which will be held August 26 - 30, 2019.
- Probabilistic modelling of dynamical systems
- The Monte Carlo idea and importance sampling
- Sequential Monte Carlo / Particle filtering
- Basic convergence theory for particle filters
- Likelihood estimation and maximum likelihood parameter inference
- Particle Markov chain Monte Carlo
- Probabilistic programming
- General SMC / SMC Samplers
The course (including successful completion of the homework assignments) corresponds to 6 ECTS credits.
The course consists of lectures and homework assignments. The homeworks will to a large extent be computer based, please bring your own laptop with some programming environment of your choice installed, e.g. Python, Julia, R, Matlab ...
Via successfully completing and handing in the hand-in assignments.
Lecture notes will be made available to the course participants,
Thomas B. Schön and Fredrik Lindsten. Learning of dynamical systems - Particle filters and Markov chain methods, Lecture notes, 2017. Available here.
Every 2 years. Earlier editions of this course have been given at Uppsala University (2017), Vrije Universiteit Brussel, Brussels, Belgium, (2012 and 2017), ICASSP (2016), Universidad Tecnica Federico Santa Maria, Valparaiso, Chile (2014), KTH (2012), and the University of Sydney, Sydney, Australia (2012).
The course takes places on August 26-30 (Monday - Friday) 2019.
See the Schedule page for more information.
The course will be held in Uppsala at Uppsala University. Exact rooms and building will be announced later.
This is a PhD level course.
Basic undergraduate courses in linear algebra, programming, probability and statistics.
For PhD students not affiliated with Uppsala University, we kindly ask you to attach a copy of certificate stating that you are admitted to PhD studies, so that we will be able to report in our system once you have passed the course. (If your postal address is not printed on the document, please state it in your email.)