Monte Carlo Simulation

Generates different plausible outcomes of your process, by repeated sampling of one or more random parameters/inputs.

Monte Carlo methods are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. The core idea is to use random samples of parameters or inputs to explore the behavior of a complex system or process

Whenever you need to make an estimate, forecast or decision where there is significant uncertainty, you'd be well advised to consider Monte Carlo simulation -- if you don't, your estimates or forecasts could be way off the mark, with adverse consequences for your decisions!

Monte Carlo Simulation Examples

Monte Carlo Simulation Function