Introduction to Monte Carlo Simulation
Monte Carlo Simulation is a technique used to generate random variables for checking the probability of risk or uncertainty in a particular system. This technique can be used to understand the impact of this risk or uncertainty on the prediction of a certain model.
Basically, in Monte Carlo simulation multiple values are assigned to the uncertain variables to achieve multiple results. Then these results are averaged to obtain an estimate.
The Monte Carlo Simulation technique is widely used in various fields including engineering, physical sciences, computational biology, statistics, finance, supply chain, artificial intelligence and supply chain..
The basics of Monte Carlo Simulation
First introduced during the Second World War, Monte Carlo Simulation is a technique widely used in various fields for modelling risks and uncertain situations in a specific system. This technique was first developed by Stanislaw Ulam while recovering from a brain surgery after the Second World War, who later collaborated with John Von Neumann to form the Monte Carlo Simulation which we use today.
Monte Carlo Simulation method focuses on constantly repeating random samples to achieve certain results. This technique uses the uncertain variable and assigns it a random value. Results are achieved by running this model. The process is repeated by assigning different variables to this uncertain variable and the final results are averaged to obtain a certain estimate.
The Monte Carlo simulation method provides the best assistance when faced with uncertainty and to avoid risks while forecasting and predicting an estimate. It is used extensively by businesses which face a lot of uncertain variables.
Monte Carlo simulation method proves to be the best as it uses multiple values to assign to the uncertain variables rather than just using one value. This helps to provide a better solution to the problem as the multiple results are averaged to reach an estimate.
