Monte Carlo algorithms

Monte Carlo algorithms are computational methods that use randomness to solve problems or make predictions. They work by generating many random samples or simulations to explore possible outcomes of a complex system. By analyzing these outcomes, they estimate probabilities, averages, or optimal solutions. These algorithms are particularly useful when traditional methods are too difficult or slow. Examples include predicting financial risks, modeling physical systems, or optimizing complex processes. Essentially, Monte Carlo algorithms leverage the power of random sampling to quantify uncertainty and make informed decisions about systems that involve chance or variability.