Estimating the covariance posterior distribution of a multivariate gaussian I need to "learn" the distribution of a bivariate gaussian with few samples, but a good hypothesis on the prior distribution, so I would like to use the bayesian approach.
I defined my prior:
$$ \mathbf{P}(\mathbf{\mu}) \sim \mathcal{N}(\mathbf{\mu_0},\mathbf{\Sigma_0}) $$
$$ \mathbf{\mu_0} = \begin{bmatrix} 0 \\ 0 \end{bmatrix} \ \ \ \mathbf{\Sigma_0} = \begin{bmatrix} 16 & 0 \\ 0 & 27 \end{bmatrix} $$
And my distribution given the hypothesis
$$ \mathbf{P}(x|\mathbf{\mu},\mathbf{\Sigma}) \sim \mathcal{N}(\mathbf{\mu},\mathbf{\Sigma}) $$
$$ \mathbf{\mu} = \begin{bmatrix} 0 \\ 0 \end{bmatrix} \ \ \ \mathbf{\Sigma} = \begin{bmatrix} 18 & 0 \\ 0 & 18 \end{bmatrix} $$
Now I know thanks to here that to estimate the mean given the data 
$$ \mathbf{P} (\mathbf{\mu} | \mathbf{x_1}, \dots , \mathbf{x_n}) \sim \mathcal{N}(\mathbf{\hat{\mu}_n}, \mathbf{\hat{\Sigma}_n})$$ 
I can compute:
$$ \mathbf{\hat{\mu}_n} = \mathbf{\Sigma_0} \left( \mathbf{\Sigma_0} + {1 \over n} \mathbf{\Sigma} \right) ^ {-1} \left( {1 \over n} \sum_{i=1}^{n} \mathbf{x_i} \right) + {1 \over n} \mathbf{\Sigma} \left( \mathbf{\Sigma_0} + {1 \over n} \mathbf{\Sigma} \right) ^{-1} \mathbf{\mu_0} $$
$$ \mathbf {\hat{\Sigma}_n} = {1 \over n} \mathbf{\Sigma_0} \left( \mathbf{\Sigma_0} + {1 \over n} \mathbf{\Sigma} \right) ^{-1} \mathbf{\Sigma} $$
Now comes the question, maybe I'm wrong, but it seems to me that $ \mathbf{\Sigma_n} $ is just the covariance matrix for the estimated parameter $\mathbf{\mu_n} $, and not the estimated covariance of my data. What I would like would be to compute also
$$ \mathbf{P} (\mathbf{\Sigma_{n_1}} | \mathbf{x_1}, \dots , \mathbf{x_n}) $$
in order to have a fully specified distribution learned from my data.
Is this possible? Is it already solved by computing $\mathbf{\Sigma_n}$ and it's just expressed in the wrong way the formula above (or I am simply misentrepreting it)? References would be appreciated. Thanks a lot.
EDIT
From the comments, it appeared that my approach was "wrong", in the sense that I was assuming a constant covariance, defined by $ \mathbf{\Sigma} $.  What I need would be to put a prior also on it, $ \mathbf{P}(\mathbf{\Sigma}) $, but I don't know what distribution I should use, and subsequently what is the procedure to update it.
 A: Ok, I found the real solution for my problem. I am posting it even if the correct answer to my (misplaced) question is the one selected.
Basically, my question explains how to estimate the mean knowing the covariance, and the answer how to estimate the covariance knowing the mean. But my actual problem was estimating with both parameters unknown.
I found the answer on Wikipedia with the derivation explained here. The multivariate normal's conjugated prior is the Normal-inverse-Wishart, that is basically a distribution over multivariate Normals.
The prior parameters that need to be specified are $\mathbf{\mu}_0$ to define the mean, the sum of pairwise deviation products $\mathbf{\Psi} = \nu_0\mathbf{\Sigma}_0$, with two scalar values $\kappa_0$ and $\nu_0$ that I would say define how confident we are on the estimation of the first two parameters respectively.
The updated distribution after observing $n$ samples of a $p$-variate Normal has the form
$$\mathbf{P}(\boldsymbol\mu, \mathbf{\Sigma} | \mathbf{X}) \sim \mathrm{NIW} \left( \frac{\kappa_0\boldsymbol\mu_0+n\mathbf{\bar{x}}}{\kappa_0+n} ,\, \kappa_0+n,\, \nu_0+n ,\,  \boldsymbol\Psi + \mathbf{C} + \frac{\kappa_0 n}{\kappa_0+n}(\mathbf{\bar{x}}-\boldsymbol\mu_0)(\mathbf{\bar{x}}-\boldsymbol\mu_0)^T \right)$$
where
$$\mathbf{\bar{x}} = {1 \over n} \sum_{i=0}^{n} \mathbf{x_i} $$
$$\mathbf{C} = \sum_{i=1}^n (\mathbf{x_i} - \mathbf{\bar{x}}) (\mathbf{x_i} - \mathbf{\bar{x}})^T $$
so my desired estimated parameters are
$$ E (\boldsymbol\mu | \mathbf{X}) = {{\kappa_0\boldsymbol\mu_0+n\mathbf{\bar{x}}} \over{\kappa_0+n} } $$
$$ E (\mathbf{\Sigma} | \mathbf{X}) = \frac{ \boldsymbol\Psi + \mathbf{C} + \frac{\kappa_0 n}{\kappa_0+n}(\mathbf{\bar{x}}-\boldsymbol\mu_0)(\mathbf{\bar{x}}-\boldsymbol\mu_0)^T }{ \nu_0 + n - p - 1}$$
A: You can do Bayesian updating for the covariance structure in much the same spirit as you updated the mean.  The conjugate prior for the covariance matrix of the multivariate-normal is the Inverse-Wishart distribution, so it makes sense to start there,
$P(\Sigma) \sim W^{-1}(\mathbf{\Psi}, \nu)$
Then when you get your sample $X$ of length $n$ you can calculate the sample covariance estimate 
$\Sigma_X = \frac{1}{n}(X-\mu)^\top(X-\mu)$
This can then be used to update your estimate of the covariance matrix
$P(\Sigma|X) \sim W^{-1}(n\Sigma_X + \mathbf{\Psi}, n + \nu)$
You may choose to use the mean of this as your point estimate for the covariance  (Posterior Mean Estimator)
$E[\Sigma|X] = \frac{n\Sigma_X + \mathbf{\Psi}}{\nu+n-p-1}$
or you might choose to use the mode (Maximum A Posteriori Estimator)
$\text{Mode}[\Sigma|X] = \frac{n\Sigma_X + \mathbf{\Psi}}{\nu+n+p+1}$
