# Completing the squares for matrices question

How does one go from

$e^{(-\frac{1}{2\sigma_n^2}(\vec{y} - X^T\vec{w})^T(\vec{y}-X^T\vec{w}))}e^{-\frac{1}{2}\vec{w}\Sigma^{-1}\vec{w}}$

to

$e^{-\frac{1}{2}(\vec{w}- w')^T(\frac{1}{\sigma^2_n}XX^T + \Sigma^{-1})(\vec{w} - w')}$

where $w' = \sigma_n^{-2}(\sigma_n^{-2}XX^T + \Sigma^{-1})^{-1}$

I'm sure the algebra works out upon expansion, but how do you come up with this?

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Meanwhile, for this answer to be of any use, let me show you a trick I came up with: from the Gaussian prior and Gaussian likelihood we expect Gaussian posterior. Gaussian posterior will have the form $$e^{-\frac{1}{2}(w - \bar w)^T\Sigma\ (w-\bar w)}$$ with $\bar w$ beeing the mean and $\Sigma$ the covariance matrix. In order to find the mean, we take the derivative of the exponent in the question (there is one transposition missing in victor's post) $$\left(\frac{1}{\sigma_n^2} (y - X^Tw)^T(y-X^Tw) + w^T\Sigma^{-1}w\right)'_w = \frac{-2}{\sigma_n^2}(y-X^Tw)^TX^T + 2w^T\Sigma^{-1},$$ and equate it to zero. Solving for $w$ we recover the $\bar w = \sigma_n^{-2}(\sigma_n^{-2}XX^T + \Sigma^{-1})^{-1}$ part.