This is an archived course. A more recent version may be available at ocw.mit.edu.

Probability and Random Variables

A diagram showing arrows connecting words describing different states of relationships, such as married, single, and "it's complicated."

The Markov model implies time spent in any state (e.g., a marriage) before leaving is a geometric random variable. Does relationship status have the Markov property? Learn more about Markov chains in Lecture 33. (Image by Professor Scott Sheffield, used with permission)

Instructor(s)

MIT Course Number

18.440

As Taught In

Spring 2011

Level

Undergraduate

Course Features

Course Description

This course introduces students to probability and random variables. Topics include distribution functions, binomial, geometric, hypergeometric, and Poisson distributions. The other topics covered are uniform, exponential, normal, gamma and beta distributions; conditional probability; Bayes theorem; joint distributions; Chebyshev inequality; law of large numbers; and central limit theorem.

Scott Sheffield. 18.440 Probability and Random Variables, Spring 2011. (Massachusetts Institute of Technology: MIT OpenCourseWare), https://ocw.mit.edu (Accessed). License: Creative Commons BY-NC-SA


For more information about using these materials and the Creative Commons license, see our Terms of Use.


Close