The probability of head for a fair coin is 1/2. ? Check out our principal component analysis tutorial.
This tutorial’s code is available on Github and its full implementation as well on Google Colab. In this article, we will go through five different examples to understand the Monte Carlo Simulation method. 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.
Moreover, even though we have unprecedented access to information, we cannot accurately predict the future. Risk analysis is part of almost every decision we make, as we constantly face uncertainty, ambiguity, and variability in our lives. 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. A Monte Carlo method is a technique that uses random numbers and probability to solve complex problems.