Covariance matrix

Dependencies:

  1. Random variable
  2. Cross-covariance matrix

Let $X = [X_1, X_2, \ldots, X_n]$ be a sequence of random variables. Then the covariance matrix of $X$, an $n$-by-$n$ matrix, is defined as the cross-covariance matrix of $X$ with itself: $\operatorname{Var}(X) = \operatorname{Cov}(X) = \operatorname{Cov}(X, X)$

Dependency for:

  1. Standard multivariate normal distribution

Info:

Transitive dependencies:

  1. /analysis/topological-space
  2. /sets-and-relations/countable-set
  3. /sets-and-relations/de-morgan-laws
  4. /measure-theory/linearity-of-lebesgue-integral
  5. /measure-theory/lebesgue-integral
  6. σ-algebra
  7. Generated σ-algebra
  8. Borel algebra
  9. Measurable function
  10. Generators of the real Borel algebra (incomplete)
  11. Measure
  12. σ-algebra is closed under countable intersections
  13. Group
  14. Ring
  15. Field
  16. Vector Space
  17. Probability
  18. Random variable
  19. Expected value of a random variable
  20. Linearity of expectation
  21. Covariance of 2 random variables
  22. Linearity of expectation for matrices
  23. Cross-covariance matrix