About:

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 are:  

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 will emphasize 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.

Finally, this event is linked to the Summer School on Bayesian filtering: fundamental theory and numerical methods (SSBF), which will be held also at ICMS on 6-10 May 2024 (i.e., the week before this workshop).

Participation:

SMC 2024 has reached maximum capacity for in-person attendance. All registered participants have been contacted by ICMS with full details including the link to pay the registration fee. If you have not received this email, please check your junk/spam inbox or get in touch.

Due to high demand, this event will now run in a hybrid format. Register above to receive the Zoom link. Please note, online attendance will allow you to watch/interact during talks only. There are no hybrid facilities in the catering area. Where speaker permission is given, talks will be recorded.