Expectation vector and covariance matrix
Definition taught in Joint distributions, part of Probability
Expectation vector and covariance matrix
| Type | Definition |
| Taught in | Joint distributions |
| Part | Probability |
For a random column vector \mathbf{X}=(X_1,\ldots,X_n)^\top the expectation vector is \boldsymbol\mu=\mathbb{E}\mathbf{X}, taken coordinate-wise, and the covariance matrix \Sigma has entries \Sigma_{ij}=\operatorname{Cov}(X_i,X_j).
Used in
- Taught in Joint distributions, where the derivation and examples are.
- All 8 questions on Joint distributions
See also
Expectation · Covariance · Joint probability mass function · Independent random variables · Joint probability density · Conditional distribution · Tower property