If $y \sim \alpha x + \beta + N(0, \sigma ^{2})$ and $x\sim N(\mu, \sigma _{x}^{2})$, what is $P(x,y)$? If y is linearly dependent on x such that the result of performing a linear regression gives you $y=\alpha x + \beta + \eta$, where the noise is normally distributed with zero mean and some prediction variance $\sigma ^{2}$, and also x is normally distributed with known mean and variance (say $\mu$ and $\sigma _{x}^{2}$), then what is the joint distribution $P(x,y)$ ?
I set out to show what my intuition was telling me, which is that, of course, the distribution should be bi-variate normal. The way I set out the proof (I can provide more details of the algebra if useful), is to explicitly calculate $P(x,y)=P(y\mid x)P(x)$, and collect all coefficients of $x^2, y^2, xy, x $ and $y$, and then try to write this as $\Lambda_{11}(x-a)^2 + 2 \Lambda_{12} (x--a)(y-b) + \Lambda_{22}(y-b)^2$, again, collecting all $x^2, y^2, xy, x $ and $y$ terms, and then matching coefficients (making use of the fact that the correlation matrix is symmetric and hence the inverse is too, and thus $\Lambda _{12}=\Lambda_{21}$).  
This appears to work, and I then note that $\Lambda $ is supposed to be the inverse of the correlation matrix $\Sigma$, so by using the explicit formulae for matrix inversion in 2d, given by
$$ \Sigma_{11} =\frac{\Lambda_{22}}{\Lambda_{11}\Lambda_{22} - \Lambda_{12}^{2}}$$
$$ \Sigma_{12} =\frac{-\Lambda_{12}}{\Lambda_{11}\Lambda_{22} - \Lambda_{12}^{2}}$$
$$ \Sigma_{22} =\frac{\Lambda_{11}}{\Lambda_{11}\Lambda_{22} - \Lambda_{12}^{2}}$$
I find that:
$$\Sigma = \begin{pmatrix}\sigma_x^2& \alpha \sigma_x^2\\ \alpha \sigma_x^2&\sigma^2 + \alpha^{2}\sigma_x^2\end{pmatrix}$$
$$a = \mu \text{ and } b = \alpha \mu + \beta$$
(and consequently, I conclude that because I can match all of the terms in $x$ and $y$ when writing the joint distribution as a bivariate Gaussian, that the constant terms must work out the same, as both forms of the distribution are normalised)
What I find very strange about this, is the off-diagonals of the correlation matrix not being affected by $\sigma $. In the limit $\sigma \to \infty$, $y$ is not dependent on $x$ so presumably their correlation should go to zero. I would expect the off-diagonals to be inversely proportional to $\sigma$. 
Have I made a mistake in my calculation or, if I haven't, what is the best way to interpret this result?
 A: $\Sigma$ is the covariance matrix, not the correlation matrix. In the correlation matrix you will see both $\sigma$ and $\sigma_x$ terms.
Intuitively, covariance is the part of variance that both variables share together which is the variance of the $X$ variable. See also the following property of covariance rule for sums of variables:
$$\text{Cov}(aX+bY,cU+dV) = ac \text{Cov}(X,U) +ad \text{Cov}(X,V) +bc \text{Cov}(Y,U) +bd \text{Cov}(Y,V) $$
and 
$$ \text{Cov}(Y,X) = \text{Cov}(\eta+\alpha X,X) = \alpha \text{Cov}(X,X) + \text{Cov}(\eta,X) =  \alpha \text{Var}(X) $$
where the last equality minimally requires zero correlation between the error term $\eta$ and the independent variable $X$ (independence will be sufficient).
So for the Pearson correlation you will have:
$$\rho_{XY} = \frac{\alpha \sigma_x^2}{\sigma_x\sigma} = \alpha \frac{\sigma_x}{\sigma} $$
which corresponds to your intuition that the correlation gets smaller for larger $\sigma$.

Regarding the question in the title, about the joint distribution, you can not determine $P(X,Y)$ without first defining the joint distribution $P(X,\eta)$. It is not enough information that $\eta$ and $X$ are individually normal distributed. This relates a bit to How to calculate conditional probability when only marginals are known?
