Particle Filter

Particle filters are a set of Monte Carlo algorithms used to approximate solutions for filtering problems in nonlinear state-space systems. They use a set of particles to represent the posterior distribution of a stochastic process given noisy and/or partial observations. Particle filter techniques provide a well-established methodology for generating samples from the required distribution without requiring assumptions about the state-space model or the state distributions. They find application in many fields, such as signal and image processing, Bayesian inference, machine learning, and more.