Solution

Past exam of the mathematics course of the University of Cambridge / 2002 / iii / Paper 32 / 2 / a / Solution

Past exam of the mathematics course of the University of Cambridge / 2002 / iii / Paper 39 / 2 / Solution

Past exam of the mathematics course of the University of Cambridge / 2002 / iii / Paper 39 / 6 / Solution

Past exam of the mathematics course of the University of Cambridge / 2003 / iii / Paper 38 / 5 / ii / a / Solution

Past exam of the mathematics course of the University of Cambridge / 2003 / iii / Paper 42 / 1 / ii / Solution

Past exam of the mathematics course of the University of Cambridge / 2003 / iii / Paper 42 / 1 / i / Solution

Past exam of the mathematics course of the University of Cambridge / 2003 / iii / Paper 42 / 3 / ii / Solution

Past exam of the mathematics course of the University of Cambridge / 2003 / iii / Paper 42 / 6 / Solution

Past exam of the mathematics course of the University of Cambridge / 2004 / iii / Paper 35 / 1 / a / Solution

Past exam of the mathematics course of the University of Cambridge / 2004 / iii / Paper 35 / 1 / c / Solution

Past exam of the mathematics course of the University of Cambridge / 2004 / iii / Paper 35 / 1 / e / Solution

Past exam of the mathematics course of the University of Cambridge / 2004 / iii / Paper 35 / 1 / g / Solution

Past exam of the mathematics course of the University of Cambridge / 2004 / iii / Paper 37 / 1 / i / Solution

Past exam of the mathematics course of the University of Cambridge / 2004 / iii / Paper 37 / 2 / ii / Solution

Past exam of the mathematics course of the University of Cambridge / 2004 / iii / Paper 43 / 1 / i / Solution

Past exam of the mathematics course of the University of Cambridge / 2004 / iii / Paper 43 / 2 / iii / Solution

Past exam of the mathematics course of the University of Cambridge / 2004 / iii / Paper 43 / 2 / i / Solution

{wiki}

= Variance-covariance matrix
{synonym}

For a random vector $X$ with finite second moments, its covariance matrix is
$$
\operatorname{cov}(X)=\mathbb E[(X-\mathbb EX)(X-\mathbb EX)^T].
$$
It is positive semidefinite, and the variance of $a^TX$ is $a^T\operatorname{cov}(X)a$.


Covariance matrix

Codex @codex  0

 Incoming links: Covariance matrix