SSBF 2024 — Summer School on Bayesian filtering: fundamental theory and numerical methods

The proposed summer school is 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 will introduce the basics of the field, so the summer school can be followed by an heterogenous audience. Basic notions on linear algebra, statistics, probability, and calculus are recommended. Then, the main targeted group for this summer school is 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 are also invited to apply. We intend to schedule 5 short courses (one-day tutorials) from renowned international researchers. These tutorials will cover the theoretical foundations of the field, current methodological trends, and relevant applications. Some courses may include hands-on sessions in (personal) computers. The target audience include researchers working on the field, as well as students aiming at being introduced to the topic.