# Multivariate normal distribution density function

I was just reading the wikipedia article about Multivariate normal distribution: http://en.wikipedia.org/wiki/Multivariate_normal_distribution

I use a little bit different notation. If $X_1,...,X_n$ are independent $\mathcal{N}(0,1)$ random variables, $X=(X_1,...,X_n)$ and $m=(m_1,...m_n)$ are n-dimensional vectors and $B$ is a $n\times n$ matrix, then $Y=m+BX$ has density function

$$f_Y(x)=\frac{1}{\sqrt{(2\pi)^n|\boldsymbol\Sigma|}} \exp\left(-\frac{1}{2}({x}-{m})^T{\boldsymbol\Sigma}^{-1}({x}-{m}) \right)$$

where $\Sigma=BB^T$

Question: In the wikipedia article its part of the definition that $\Sigma$ is the covariance matric from $Y$, i.e $\sum_{ij}=Cov(Y_i,Y_j)$, but why? How can this be proved.

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By the definition, any covariance matrix $\Sigma$ consists of $\Sigma_{i,j} = Cov(X_i, X_j) = E[(X_i - \mu_i)(X_j - \mu_j)]$, where randoms $X_i$ and $X_j$ have expectations $E[X_i] = \mu_i$ and $E[X_j] = \mu_j$ accordingly. So your problem is to prove $Cov(m_i + \mathbf{b}_i \mathbf{X}, m_j + \mathbf{b}_j \mathbf{X}) = (B \cdot \Sigma \cdot B^\top)_{i,j}$, where $B$ is non-singular matrix with rows $\mathbf{b}_i = (b_{i,1}, \ldots, b_{i,n})$, $1 \le i \le n$ and $\mathbf{X} = (X_1, \ldots, X_n)$ is $n$-dimensional random, $(m_1, \ldots, m_n)$ is vector of scalars.

Let $\mathbf{X}$ has the covariance $n \times n$-matrix $\Sigma$, which is symmetric.

Note, $E[m_i + \mathbf{b}_i \mathbf{X}] = m_i + \mathbf{b}_i \mathbf{\mu}$, where $\mathbf{\mu} = (\mu_1, \ldots, \mu_n)$. Expectation operator $E$ is linear. So, the proof:

$Cov(m_i + \mathbf{b}_i \mathbf{X}, m_j + \mathbf{b}_j \mathbf{X}) = E[(\mathbf{b}_i (\mathbf{X} - \mathbf{\mu}))(\mathbf{b}_j (\mathbf{X} - \mathbf{\mu}))] = E[\sum_{t=1}^n \sum_{p=1}^n b_{i,t} b_{j,p} (X_t - \mu_t)(X_p - \mu_p)] = \sum_{t=1}^n \sum_{p=1}^n b_{i,t} b_{j,p} E[(X_t - \mu_t)(X_p - \mu_p)] = \sum_{t=1}^n b_{i,t} \left(\sum_{p=1}^n b_{j,p} \Sigma_{p,t}\right) = \sum_{t=1}^n b_{i,t} \left( B \cdot \Sigma \right)_{j,t} = \left(B \cdot \Sigma \cdot B^\top\right)_{i,j}$

So the covariance matrix of $\mathbf{m} + B \cdot \mathbf{X}$ is $B \cdot \Sigma \cdot B^\top$, where $Cov(\mathbf{X}) = \Sigma$.

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Still, I do not see the relation $\sum_{ij}=Cov(Y_i,Y_j)$, I think this can be proved formally somehow. –  Babla May 18 '13 at 15:20
@Babla $Cov(\mathbf{c} + B \cdot \mathbf{X}, \mathbf{c} + B \cdot \mathbf{X}) = B \cdot \Sigma \cdot B^\top$, where $\Sigma$ is covariance matrix of random Gaussian vector $\Sigma$. May be I do not understand your problem... –  Piotr Semenov May 18 '13 at 15:26
I think this is what we need to show. $Cov(Y_i,Y_j)=Cov(m+BX_i,m+BX_j)=Cov(BX_i,BX_j)=?$ –  Babla May 18 '13 at 15:47
@Babla I see. I have rewritten my answer according to your comment. –  Piotr Semenov May 18 '13 at 16:56
Thanks you, thats what I was looking for. I have one more question. If I have two ranndom variables $Y_1$ ($\mathcal{N}(\mu_1,\sigma_1^2$)) and $Y_2$ ($\mathcal{N}(\mu_2,\sigma_2^2$) and we also know that $Cov(Y_1,Y_2)$, how can follow from the observations above that $Y_1$ and $Y_2$ are independent? –  Babla May 18 '13 at 17:15