Probability and Random Variables
As taught in: Spring 2011
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)
Instructors:
Prof. Scott Sheffield
MIT Course Number:
18.440
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.
