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Monte Carlo Simulation An In-depth Tutorial with Python
An in-depth tutorial on the Monte Carlo Simulation methods and applications with Python
Author(s): Pratik Shukla, Roberto Iriondo
Last updated January 8, 2021
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. This tutorial’s code is available on Github and its full implementation as well on Google Colab.
📚 Check out our principal component analysis tutorial. 📚
Applications:
- Finance.
- Project Management.
- Energy.
- Manufacturing.
- Engineering.
- Research and Development.
- Insurance.
- Oil and Gas.
- Transportation.
- Environment.
