## What is the Monte Carlo Simulation?

A Monte Carlo method is a technique that uses random numbers and probability to solve complex problems. The Monte Carlo simulation, or probability simulation, is a technique used to understand the impact of risk and uncertainty in financial sectors, project management, costs, and other forecasting **machine learning** models.

Risk analysis is part of almost every decision we make, as we constantly face uncertainty, ambiguity, and variability in our lives. Moreover, even though we have unprecedented access to information, we cannot accurately predict the future.

The Monte Carlo simulation allows us to see all the possible outcomes of our decisions and assess risk impact, in consequence allowing better decision making under uncertainty.

**In this article, we will go through five different examples to understand the Monte Carlo Simulation method.**

## a. Coin Flip Example:

The probability of head for a fair coin is 1/2. However, is there any way we can prove it experimentally? In this example, we are going to use the Monte-Carlo method to simulate the coin-flipping iteratively 5000 times to find out why the probability of a head or tail is always 1/2. If we repeat this coin flipping many, many more times, then we can achieve higher accuracy on an exact answer for our probability value.

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