Monte Carlo method

Monte Carlo methods, also called the Monte Carlo experiments or Monte Carlo simulations, are a broad class of computationalalgorithms based on repeatedrandom sampling for obtaining numerical results, conceptualized by PolishmathematicianStanisław Ulam. The underlying concept is to use randomness to solve deterministic problems.

Monte Carlo methods are mainly used in three distinct problem classes: optimization, numerical integration, and non-uniform random variate generation, available for modeling phenomena with significant input uncertainties, e.g. risk assessments for nuclear power plants. Monte Carlo methods are often implemented using computer simulations. They can provide approximate solutions to problems too complex for mathematical analysis.