Particle Filters
Particle filters, also known as sequential Monte Carlo methods, are a class of algorithms used for state estimation in dynamic systems, particularly in non-linear and non-Gaussian scenarios. They approximate probability distributions using a set of random samples (particles) with associated weights, which are updated recursively as new observations arrive. This makes them highly effective for tracking and localization problems where traditional Kalman filters fail.
Developers should learn particle filters when working on robotics, autonomous vehicles, or any application requiring real-time state estimation in complex environments, such as sensor fusion or object tracking. They are especially valuable in fields like computer vision, where systems must handle non-linear dynamics and multi-modal distributions, providing a robust alternative to analytical methods.