Sequential Monte Carlo Methods (SMC) are a set of computational techniques used for estimating the state of a dynamic system over time. They work by generating a set of random samples, or particles, that represent possible states of the system. These particles are updated sequentially as new data becomes available, allowing for real-time estimation and tracking. SMC methods are particularly useful in fields like signal processing, robotics, and finance, where systems are often subject to uncertainty and noise. By combining the particles' weights based on observed data, SMC provides a flexible framework for approximating complex probability distributions and making predictions.