Probability

Lesson 5 of 14

Random Variables and Expectation

PMFs, CDFs, expected value, variance, and linearity of expectation.

Random variables

A random variable is a function from outcomes to numbers: X:Ω→RX: \Omega \to \mathbb{R}. Roll two dice, let XX = sum: XX maps (3,4)↦7(3,4) \mapsto 7.

Discrete variables have a PMF p(x)=P(X=x)p(x) = P(X = x); continuous ones a density (next lessons). The CDF F(x)=P(X≤x)F(x) = P(X \le x) works for both and fully characterizes the distribution.