Sequential importance sampling

Sequential importance sampling (SIS) is a statistical method used to estimate complex probabilities that evolve over time. It works by generating many possible scenarios (samples) of how a process might unfold, then assigning each scenario a weight based on how well it matches observed data. As new data comes in, the method updates the weights and refocuses on the most relevant scenarios, improving the estimate’s accuracy. This approach is especially useful in fields like signal processing or finance, where understanding evolving systems with uncertainty is critical. Essentially, SIS helps us efficiently approximate complex, changing probabilities through targeted sampling.