Sequential Monte Carlo (SMC) as a class of online learning algorithms, can handle general non-linear and non-Gaussian modeling and inference. Hence, it has been widely used in signal and image processing, Bayesian inference, risk analysis and rare event sampling, engineering and robotics, bioinformatics, phylogenetics, mathematical finance, etc. This section will present explainable and interpretable SMC algorithms that are suitable for Nonlinear and Non-Gaussian data science challenges, with rigorous performance guarantee and important scientific applications.

Organiser: Prof. Ning Ning 

Chair: Dr Ning Ning 

Speaker: Nicolas Chopin 

Speaker: Dan Crisan 

Speaker: Prof. Jun Liu 

Speaker: Pierre Del Moral