Sequential Monte Carlo (SMC) methods, also known as particle filters or particle methods, have become popular and powerful tools for computational inference in complex probabilistic models used in many and varied fields and applications. The research community includes practitioners and theoreticians at the intersection of statistics, computer science, electrical engineering, and applied mathematics. The interest in SMC methods have rapidly grown in the last decade, jointly with the new challenges and the enormous potential of SMC to tackle high-impact problems in applied sciences (e.g., meteorology, biomedicine, robotics, etc.). The main objectives of the meeting were:  

1. to bring together researchers developing and using SMC methods in a diversity of scientific and engineering fields, both in academia and the industry, and

2. to open the field to researchers and users who are new to SMC methods. We emphasized the interaction of SMC with related areas of research (such as machine learning or data science) where computational inference plays a key role.

Below, we provide a list of key theoretical and methodological topics, as well as application areas.

  Theory & Methodology

  • Particle filtering/SMC methods

  • Kalman filtering

  • Data assimilation

  • State space models

  • Numerical methods for stochastic partial differential equations (SPDEs)

  • Adaptive importance sampling

  • Sequential Markov-chain Monte Carlo (MCMC) methods

  • Stability of optimal filters

  • Multi-level Monte Carlo

  Applications

  • Multi-target tracking

  • Radar and sonar signal processing

  • Localization and tracking with sensor networks

  • Numerical weather prediction

  • Climate modeling and prediction

  • Biomedical signal processing

  • Astrophysics and cosmology

  • Smart grids and industrial applications

  • Quantitative finance

Finally, this event was linked to the Summer School on Bayesian filtering: fundamental theory and numerical methods (SSBF), also held at ICMS on 6-10 May 2024.