Monte Carlo differential estimators utilize Monte Carlo sampling to approximate integrals and derivatives, especially in high-dimensional or complex distributions. By repeatedly drawing samples from the underlying distribution and evaluating the target function at these samples, the method generates an unbiased estimate of the desired quantity. The expected value of the estimates converges to the true value as the number of samples increases, making it a powerful tool for numerical integration and solving differential equations.
