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)


MIT Course Number


As Taught In

Spring 2011



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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), (Accessed). License: Creative Commons BY-NC-SA

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