# multivariate distributions

(2 hours to learn)

## Summary

Multivariate distributions are a way of representing the dependencies between multiple random variables.

## Context

This concept has the prerequisites:

- random variables (Multivariate distributions are a way of representing dependencies between random variables.)
- multiple integrals (Multiple integrals are needed to compute probabilities associated with continuous multivariate distributions.)

## Core resources (read/watch one of the following)

## -Free-

→ Mathematical Monk: Probability Primer (2011)

## -Paid-

→ A First Course in Probability

An introductory probability textbook.

Location:
Section 6.1, "Joint distribution functions," pages 258-267

Other notes:

- The parts about multivariate cumulative distribution functions are optional.

→ Probability and Statistics

An introductory textbook on probability theory and statistics.

- Section 3.4, "Bivariate distributions," pages 118-126
- Section 3.5, "Marginal distributions," up to "Independent random variables," pages 128-131

Other notes:

- The parts about multivariate cumulative distribution functions are optional.

→ Mathematical Statistics and Data Analysis

An undergraduate statistics textbook.

- Section 3.1, "Introduction," pages 71-72
- Section 3.2, "Discrete random variables," pages 72-75
- Section 3.3, "Continuous random variables," pages 75-84

## See also

-No Additional Notes-