Sequential Monte Carlo (SMC) methods, also known as particle filters, are a set of simulation-based algorithms used for estimating the posterior distribution of dynamic systems that evolve over time. They are particularly effective in handling non-linear and non-Gaussian models, making them valuable for real-time data assimilation and Bayesian inference in complex systems.