Lecture 16: Using Randomness to Solve Non-random Problems

author: John Guttag, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, MIT
recorded by: Massachusetts Institute of Technology, MIT
published: Oct. 29, 2012,   recorded: March 2011,   views: 2403
released under terms of: Creative Commons Attribution Non-Commercial Share Alike (CC-BY-NC-SA)
Categories

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

This lecture starts by defining normal (Gaussian), uniform, and exponential distributions. It then shows how Monte Carlo simulations can be used to analyze the classic Monty Hall problem and to find an approximate value of pi.

Topics covered: Gaussian distributions, analytical models, simulations, exponential growth, probability, distributions, Monty Hall problem.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: