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Sequential Monte Carlo Methods2025VT
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Course plan
Schedule
The course is given during two intensive sessions.
First session: Feb 3-4 (Mon-Tues), to introduce state space models and basic
SMC algorithms
Second session: Feb 26-28 (Wed-Fri, with half day on Friday), focusing on more
advanced methods and applications of SMC beyond state space models
Recommended for
PhD students with interest in probabilistic modelling, MCMC methods, Monte Carlo methods, dynamical systems or just think that SMC sounds like an interesting topic.
The course was last given
2021
Goals
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, with applications in conditional diffusion-based generative models, LLMs, graphical model inference, probabilistic programming, etc.
Prerequisites
Basic knowledge of statistics and probability.
Basic knowledge of programming in any language.
Organization
Johan Alenlöv (LiU/IDA)
Fredrik Lindsten (LiU/IDA)
Zheng Zhao (LiU/IDA)
Content
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
General SMC / SMC Samplers
Literature
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
Chrisitan A. Naesseth, Fredrik Lindsten and Thomas B. Schön. Elements of
Sequential Monte Carlo
Lectures
18 hours of lectures are planned, see course webpage for preliminary plan of lectures.
Examination
Via successfully completing and handing in the hand-in assignments.
Examiner
Johan Alenlöv (LiU/IDA)
Credits
6 ECTS
Comments
Page responsible: Anne Moe
