Python Specials #3 Monte Carlo Simulation
In this example, we simulate flipping a fair coin (p=0.5) 10,000 times (num_trials). We use numpy to generate a binomial distribution with n=1 and p=0.5 to simulate each coin flip. We then calculate the running average of the results and plot it against the number of trials. Finally, we plot a horizontal line at the true probability of success (p=0.5) for comparison.
Note that Monte Carlo simulations can be used to simulate a wide range of probability distributions and processes, not just coin flips. The basic idea is to generate a large number of random samples from the distribution of interest and use these samples to estimate probabilities, expected values, and other properties of the distribution.
Thanks for watching. For more videos and to support us, you can subscribe and follow our channel. Good luck...
All Coder & Maker videos : • ALL VIDEOS
More Python Specials videos: • Python Specials
More Python Examples videos: / playlist
list=PLN6LC3AB64ggeDF1_fR-77gdlNrtz-8VQ
Python Basics Videos : • Python Basics
Python Turtle Graphics Module: • Python Turtle Graphics
Python Matplotlib Module Tutorials: • Python Matplotlib Module
Python Numpy Module Tutorials: • Python Numpy Module
Python Random Module Tutorials: • Python Random Module
Python Thonny Graphics Tutorials: • Python Turtle Tutorials
Download Thonny Ide: https://thonny.org