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-