Monte carlo methods is a class of computational algorithms that employ repeated random sampling to obtain numerical results or model complex phenomena. Characterized by their probabilistic foundation, these methods are essential for approximating solutions to mathematical or physical problems that are analytically intractable or computationally prohibitive using deterministic techniques, such as simulating stochastic processes or estimating quantities in high-dimensional spaces.