Sequential Monte Carlo
Sequential Monte Carlo (SMC) is a class of computational algorithms used for approximate Bayesian inference, particularly for state-space models and time-series data. It involves recursively updating a set of weighted samples (particles) to approximate posterior distributions as new data arrives sequentially. SMC methods are widely applied in fields like signal processing, robotics, and finance for tasks such as filtering, smoothing, and parameter estimation.
Developers should learn SMC when working on real-time systems or dynamic models where data arrives incrementally, such as in tracking applications (e.g., GPS, object detection) or financial forecasting. It is especially useful for handling non-linear and non-Gaussian models where traditional methods like Kalman filters fail, providing a flexible and scalable approach to probabilistic inference in complex scenarios.