Consider a scenario where you're expected to solve a very tricky probability problem but you don't know how to solve it or another scenario where the probability problem requires a specific domain knowledge in which you're not an expert. Monte Carlo Simulation will come to your rescue in such scenarios, it is a method in which we simulate the random experiment using computational algorithms. It is usually a much simpler method to find the required probability compared to the theoretical (or mathematical) methods, however it is not as accurate as the mathematical method and it can be slow & computationally expensive.
Lets understand Monte Carlo Simulation using examples!
Lets start with one of the simplest and most commonly sited example of Monte Carlo Simulation and once we get a hang of it. We'll solve a tricky problem using the same technique.