# multivariate CDF

(1.7 hours to learn)

## Summary

Multivariate cumulative distribution functions (CDFs) are a way of characterizing multivariate distributions which generalize the univariate CDF.

## 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.)
- cumulative distribution function (Joint distributions can be represented in terms of the joint CDF.)
- higher-order partial derivatives (Higher order partial derivatives are used to recover the joint PDF from the joint CDF.)

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

## -Paid-

→ 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

→ A First Course in Probability

An introductory probability textbook.

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

## See also

-No Additional Notes-