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Covariance Matrix

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Definition
Probability

Definition

Given a random vector Xn=(X1,,Xn)X^{n}=(X_{1},\cdots,X_{n}), the covariance matrix of XnX^{n}, KXi,XjK_{X_{i},X_{j}}, is defined as follows KXi,Xj=(\mboxVar(X1)\mboxCov(X1,X2)\mboxCov(X1,Xn)\mboxCov(X2,X1)\mboxVar(X2)\mboxCov(X2,Xn)\mboxCov(Xn,X1)\mboxCov(Xn,X2)\mboxVar(Xn))K_{X_{i},X_{j}}= \begin{pmatrix} \mbox{Var}(X_{1}) & \mbox{Cov}(X_{1},X_{2}) & \cdots & \mbox{Cov}(X_{1},X_{n}) \\ \mbox{Cov}(X_{2},X_{1}) & \mbox{Var}(X_{2}) & \cdots & \mbox{Cov}(X_{2},X_{n}) \\ \vdots & \vdots & \ddots & \vdots \\ \mbox{Cov}(X_{n},X_{1}) & \mbox{Cov}(X_{n},X_{2}) & \cdots & \mbox{Var}(X_{n}) \end{pmatrix}

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