Expectation
Chapter 13: Probability and Random Variables — Expectation, Variance & Covariance
From the book
Chapter 13: Probability and Random Variables. In the chapter mind map this icon labels Expectation, Variance & Covariance. The discussion below is excerpted and lightly edited from § Definition: Covariance in Mathematics for AI and Machine Learning.
For random variables $X \in \mathbb{R}$ and $Y \in \mathbb{R}$, the covariance is defined as
What this drawing shows
What you see. Represents averaging a random variable under a probability distribution, the central operation behind moments and risk.
In the mind map. Chapter 13 — Expectation, Variance & Covariance. See From the book above for definitions, figures, and worked examples.
Where to read next
Read the full definitions, figures, and worked examples in Chapter 13: Probability and Random Variables — see the mind-map node Expectation, Variance & Covariance.