Sequential Monte Carlo Methods

Submitted by alenlov on December 16, 2024 - 11:28

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.

This is an intensive course which will be held during two sessions on-site at Linköping University:

  • First session: Feb 3-4 (Mon-Tues before the WASP winter conference), 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

For more information about the course and how to register please see the course homepage https://www.ida.liu.se/divisions/stima/fokurser/smc2025/

Course Data
Type of schedule: 
Travel friendly schedule
University: 
Linköping
Level: 
PhD
Credits (ECTS): 
6.00
Offered: 
2025:1