Monte Carlo Estimation
A technique used within AI to approximate the probability of an event by running several simulations and observations.
Monte Carlo estimation is a process often used within the sphere of AI, specifically in the areas of Machine Learning (ML) and reinforcement learning. This method is employed to approximate the probability or expectation of an event by running a number of random simulations, or Monte Carlo experiments, and then taking the mean of those outcomes. The result is an average estimation that gets more accurate the more iterations are performed. Despite the computational cost, they are preferred for problems where analytical solutions are inconvenient or impossible to achieve.
The Monte Carlo method was first developed by scientists working on the atomic bomb during the Manhattan Project in the 1940s. The name itself is derived from the Monte Carlo Casino in Monaco where one of the developers, Nicholas Metropolis, was known to gamble. It was later employed heavily in AI and ML algorithms during the AI boom in the late 20th and early 21st century.
Notable figures that contributed to the development of the Monte Carlo estimation method include Stanislaw Ulam, John von Neumann, and Nicholas Metropolis. Its modification and application within the AI domain was influenced by figures such as Robert Schapire, Yoav Freund, and Judea Pearl among others.