Intuitive Explanation of Monte Carlo Simulation

Simple Explanation of Monte Carlo Simulation and Implementation using Python

Monte Carlo Simulation Origin

The origins of the Monte Carlo simulation technique trace back to the 1940s during the Manhattan Project, where physicist Stanislaw Ulam faced challenges in calculating the probability of neutron fission within nuclear reactors. It was during a vacation in Monaco that Ulam, together with mathematician John von Neumann, formulated the idea of the Monte Carlo method. Drawing inspiration from the randomness inherent in gambling games, they employed simulation of neutron behavior to tackle this intricate issue. The method was aptly named after Monte Carlo as it orginated in the bustling casinos of Monaco, specifically a game of chance called roulette, made famous at Monte Carlo resorts.

What is Monte Carlo Simulation?

Monte Carlo is a mathematical technique used to predict the potential outcomes of an uncertain event. It generates a range of possible outcomes by accounting for the randomness inherent in the event’s variables.

Monte Carlo simulations enhance our understanding of the risks and uncertainties tied to unpredictable variables, such as stock prices or inventory levels, through a series of…