A Simple Solution for Really Hard Problems: Monte Carlo Simulation

RiskByNumbers October 12, 2023
Video Thumbnail
RiskByNumbers Logo

RiskByNumbers

@riskbynumbers

About

Civil engineering professor by day. Risk quantifier 24/7. ============================================= UPDATE (July 8, 2024): We now have a RiskByNumbers blog available at riskbynumbers.org! ============================================= Hello and welcome to RiskByNumbers! I am a professor excited to share educational resources around probability, statistics, optimization methods, algorithms, and programming to a broad audience. Outside of YouTube, you can currently find me in Vancouver, Canada at the University of British Columbia. If this content resonates with you, or if you have further questions, leave a comment or reach out to me directly (while the channel is still relatively new). Email: [email protected] LinkedIn Bio: https://www.linkedin.com/in/omar-swei/

Video Description

Today's video provides a conceptual overview of Monte Carlo simulation, a powerful, intuitive method to solve challenging probability questions. And we get to see how we can use it to answer a realistic question in Python! 0:00 Monte Carlo Applications 0:22 Party Problem: What is The Chance You'll Make It? 1:16 Monte Carlo Conceptual Overview 3:00 Monte Carlo Simulation in Python: NumPy and matplotlib 5:02 Party Problem: What Should You Do? You can access the dataset and Jupyter Notebook used to run these scripts, which includes proper commenting, at: https://github.com/RiskByNumbers/Monte-Carlo-Simulation-Video. #montecarlosimulation #probability #datascience #python #numpy #tutorial #sampling ===================== Hello, and welcome to RiskByNumbers! I am a professor sharing educational resources around probability, statistics, optimization methods, algorithms, and programming to a broad audience. Outside of Youtube, you can currently find me in Vancouver, Canada at the University of British Columbia. Thank you, and I look forward to seeing you in future videos! Email: [email protected]. LinkedIn: https://www.linkedin.com/in/omar-swei/

You May Also Like