To beat any random game, is it enough just to have a sufficient number of attempts? Not necessarily.

Monte Carlo methods are computational techniques used to solve problems through the simulation of random samples. The goal of these methods is to estimate outcomes based on statistics and probability. The tutorial presents various aspects of applying these methods, including their operation, advantages, and examples of applications across different fields. Key concepts include generating random variables and estimating expected values. There are numerous applications of Monte Carlo methods, ranging from finance to engineering, which makes them a universal analytical tool. Understanding these techniques requires a basic knowledge of statistics and probability theory, but they serve as fundamental concepts for anyone looking to explore more complex analytical and simulation models.