Monte Carlo Methods - how to predict the future using simulations?
Monte Carlo methods are statistical techniques that utilize random sampling to obtain numerical results. These methods are particularly useful in approaching complex problems that may be deterministic in nature but are difficult to solve analytically. By running simulations multiple times with random input values, one can estimate the probabilities of different outcomes and derive solutions based on these estimations. The article highlights several applications of Monte Carlo methods, such as option pricing in finance, risk assessment, and even in various scientific fields. Tools like Python, R, and MATLAB are commonly used to implement these simulations, making Monte Carlo methods accessible to a broad audience of users. Overall, Monte Carlo methods provide a powerful way to address uncertainty and variability in problem-solving.