SIR (Sequential Importance Resampling)

Sequential Importance Resampling (SIR) is a method used in statistical modeling to estimate the state of a system over time, especially when the system is uncertain or noisy. It works by representing possible states with a group of samples called particles. As new data arrives, each particle’s importance is updated based on how well it predicts this data. Particles with higher importance are more likely to be retained, while less likely ones are replaced through resampling. This process allows the model to adapt to changes and maintain an accurate estimate of the system’s true state over time.