About:

The summer school was devoted to the fundamental theory, state-of-the-art methodologies and real-world applications of Bayesian filtering, including sequential Monte Carlo (SMC) algorithms and other popular techniques, such as sigma-point methods for nonlinear Kalman filtering, Gaussian-mixture filters and others. We introduced the basics of the field, so the summer school could be followed by an heterogenous audience. Basic notions on linear algebra, statistics, probability, and calculus are recommended. Then, the main targeted group for the summer school was PhD students with background in mathematics, statistics, physics, engineering, or computer science (but not only). Other students (e.g., last year MSc students) and early-career academics with similar background were also invited to apply. We intended to schedule 5 short courses (one-day tutorials) from renowned international researchers. These tutorials covered the theoretical foundations of the field, current methodological trends, and relevant applications. Some courses included hands-on sessions in (personal) computers. The target audience included researchers working on the field, as well as students aiming at being introduced to the topic.

This summer school was aligned with the 6th Workshop on Sequential Monte Carlo Methods (SMC 2024), also held at ICMS on 13-17 May 2024.