Particle filter

Also known as sequential Monte Carlo, a particle filter performs Bayesian recursive filtering for nonlinear, non-Gaussian state-space models. It represents the posterior distribution of the hidden state as a weighted set of random samples (particles), updating weights via importance sampling at each time step. Widely used in robotics, tracking, and financial modeling.