given zero mean Gaussian prior as $\beta~ \sim(0,\Sigma p)$
inference is given by $\log p(\beta \mid y, X)=\log p(y \mid X, \beta)+\log p(\beta)-\log (y\mid X)$
I can't understand how to get the following lines? can anyone prove it? $-\log p(\beta \mid y, X)=\frac{1}{2 \sigma_{n}^{2}}(y-X \beta)^{\top}(y-X \beta)+\frac{1}{2} \beta^{\top} \Sigma_{p}^{-1} \beta=\frac{1}{2}(\beta-\bar{\beta})^TA(\beta-\bar{\beta})$
where A is equal to $\frac{1}{\sigma^{2} n} x^{\top} x+\Sigma_{p}^{-1}$