23rd Oct 2023

Monte Carlo approximation is a statistical technique that leverages random sampling and probability principles to estimate complex numerical values, making it especially useful for problems marked by uncertainty or the absence of precise analytical solutions.

In this approach, a substantial number of random samples are generated, drawn from probability distributions representing the problem’s inherent uncertainty. Each of these random samples is used as input for the problem, and their resulting outcomes are recorded. As more samples are considered, the estimated values converge closer to the true value of the problem, guided by the law of large numbers, ensuring greater accuracy with a larger sample size. Monte Carlo approximation proves to be a robust and adaptable method, providing accurate estimates and valuable insights for addressing intricate problems, particularly those involving uncertainty and complex systems.