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Sequential Monte Carlo (SMC) methods provide an attractive way to estimate unknown quantities from noisy observations, as they are flexible, easy-to-implement and have reduced computational burden compared to Markov Chain Mote Carlo methods. Last decade has witnessed an explosion of scientific articles on SMC methods and their applications to inverse problems and image reconstruction, thanks to the ever-increasing computing power. The aim of this minisymposium is to bring together experts working in this field and introduce the latest algorithmic developments and applications of SMC methods pertaining to multiple target tracking, brain connectivity estimation and multimodal sensor analysis, among others.