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.).