Rao-Blackwellization

Rao-Blackwellization is a statistical technique that improves the accuracy of an estimate by reducing its variance. It works by using a known relationship within the data—such as a conditional expectation—to refine a preliminary estimate, making it more precise. Essentially, it leverages additional information to produce a better, more reliable estimate without introducing bias. This method is particularly useful in complex models and probabilistic algorithms like Monte Carlo simulations, where it helps generate more accurate results efficiently.