Monte Carlo Methods

Monte Carlo methods rely on repeated random sampling to compute results, especially useful for optimization, numerical integration, and generating draws from probability distributions. They can solve complex problems by simulating many random scenarios and analyzing the aggregate results. While they have high variance, they are unbiased and particularly effective in episodic tasks where learning from complete episodes of experience is possible.

Financial modeling, particle physics simulations, optimization problems, computer graphics, and risk analysis.