corrected some pages

Several small corrections were done to several proofs and definitions.

Author: JoramSoch

P/bvn-pdf.md

@@ -40,7 +40,7 @@ $$ \label{eq:mvn-pdf}
 f_X(x) = \frac{1}{\sqrt{(2 \pi)^n |\Sigma|}} \cdot \exp \left[ -\frac{1}{2} (x-\mu)^\mathrm{T} \Sigma^{-1} (x-\mu) \right] \; .
 $$
 
-Plugging in $n = 2$, $\mu = \left[ \begin{matrix} \mu_1 \\\\ \mu_2 \end{matrix} \right]$ and $\Sigma = \left[ \begin{matrix} \sigma_1^2 \& \sigma_{12} \\\\ \sigma_{12} \& \sigma_2^2 \end{matrix} \right]$, we obtain:
+Plugging in $n = 2$, $\mu = \left[ \begin{matrix} \mu_1 \\\\ \mu_2 \end{matrix} \right]$ and $\Sigma = \left[ \begin{matrix} \sigma_1^2 & \sigma_{12} \\\\ \sigma_{12} & \sigma_2^2 \end{matrix} \right]$, we obtain:
 
 $$ \label{eq:bvn-pdf-s1}
 \begin{split}

P/bvn-pdfcorr.md

@@ -42,7 +42,7 @@ $$
 where $\rho$ is the [correlation](/D/corr) between $X_1$ and $X_2$.
 
 
-**Proof:** Since $X$ follows a special case of the [multivariate normal distribution, its covariance matrix is](/P/mvn-corr)
+**Proof:** Since $X$ follows a special case of the [multivariate normal distribution, its covariance matrix is](/P/mvn-cov)
 
 $$ \label{eq:cov-X}
 \mathrm{Cov}(X) = \Sigma = \left[ \begin{matrix} \sigma_1^2 & \sigma_{12} \\ \sigma_{12} & \sigma_2^2 \end{matrix} \right]