A method for tracking a hidden state with many random guesses.
Particle Filter is like finding your lost dog at a park. Drop guesses like tennis balls on a map, then keep the ones near fresh paw prints.
It helps robots and apps track position. It can still follow the state through messy sensor noise.
Kalman Filter
Both estimate state, but Particle Filter handles nonlinear paths better.
HMM
Particle Filter can use samples to estimate the hidden state in an HMM.
MCMC
Both use sampling, but Particle Filter is built for live tracking.
SLAM
SLAM can use Particle Filter to locate a robot through noisy motion and sensor data.