Integrating out parameter with improper prior I got this problem while I was reading the book "Machine Learning: A Probabilistic Perspective" by Kevin Murphy. It is in section 7.6.1 of the book.
Assume the likelihood is given by 
$$
\begin{split}
p(\mathbf{y}|\mathbf{X},\mathbf{w},\mu,\sigma^2) & = \mathcal{N}(\mathbf{y}|\mu+\mathbf{X}\mathbf{w}, \sigma^2\mathbf{I}_N) \\
    & \propto (-\frac{1}{2\sigma^2}(\mathbf{y}-\mu\mathbf{1}_N - \mathbf{X}\mathbf{w})^T(\mathbf{y}-\mu\mathbf{1}_N - \mathbf{X}\mathbf{w}))
\end{split}
\tag{7.53}
$$
$\mu$ and $\sigma^2$ are scalars. $\mu$ serves as an offset. $\mathbf{1}_N$ is a column vector with length $N$.
We put an improper prior on $\mu$ of the form $p(u) \propto 1$ and then integrate it out to get 
$$
p(\mathbf{y}|\mathbf{X},\mathbf{w},\sigma^2) \propto (-\frac{1}{2\sigma^2}||\mathbf{y}-\bar{y}\mathbf{1}_N - \mathbf{X}\mathbf{w}||_2^2)
\tag{7.54}
$$
where $\bar{y}=\frac{1}{N}\sum_{i=1}^{N}y_i$ is the empirical mean of the output. 
I tried to expand the formula (last line in $7.53$) to integrate directly but failed.
Any idea or hint on how to derive from $(7.53)$ to $(7.54)$?
 A: This calculation assumes that the columns of the design matrix have been centred, so that:
$$(\mathbf{Xw}) \cdot \mathbf{1}_N = \mathbf{w}^\text{T} \mathbf{X}^\text{T} \mathbf{1}_N = \mathbf{w}^\text{T} \mathbf{0} = 0.$$
With this restriction you can rewrite the quadratic form as a quadratic in $\mu$ plus a term that does not depend on $\mu$ as follows:
$$\begin{equation} \begin{aligned}
|| \mathbf{y} - \mu \mathbf{1}_N - \mathbf{X} \mathbf{w} ||^2 
&= || \mathbf{y} - \bar{y} \mathbf{1}_N - \mathbf{X} \mathbf{w} + (\bar{y} - \mu) \mathbf{1}_N ||^2 \\[6pt]
&= || \mathbf{y} - \bar{y} \mathbf{1}_N - \mathbf{X} \mathbf{w} ||^2
+ 2 (\bar{y} - \mu) (\mathbf{y} - \mu \mathbf{1}_N - \mathbf{X} \mathbf{w}) \cdot \mathbf{1}_N
+ (\bar{y} - \mu)^2 || \mathbf{1}_N ||^2 \\[6pt]
&= || \mathbf{y} - \bar{y} \mathbf{1}_N - \mathbf{X} \mathbf{w} ||^2
- 2 n (\bar{y} - \mu)^2 + n (\bar{y} - \mu)^2 \\[6pt]
&= || \mathbf{y} - \bar{y} \mathbf{1}_N - \mathbf{X} \mathbf{w} ||^2
- n (\mu - \bar{y})^2. \\[6pt]
\end{aligned} \end{equation}$$
Hence, with the improper prior $\pi(\mu) \propto 1$ you have:
$$\begin{equation} \begin{aligned}
p(\mathbf{y}|\mathbf{X},\mathbf{w},\sigma^2) 
&= \int \limits_\mathbb{R} p(\mathbf{y}|\mathbf{X},\mathbf{w},\mu,\sigma^2) \pi(\mu)  \ d \mu \\[6pt]
&\overset{\mathbf{y}}{\propto} \int \limits_\mathbb{R} \exp \Big( -\frac{1}{2\sigma^2} || \mathbf{y}-\mu\mathbf{1}_N - \mathbf{X}\mathbf{w} ||^2 \Big) \ d \mu \\[6pt]
&= \exp \Big( -\frac{1}{2\sigma^2} || \mathbf{y} - \bar{y} \mathbf{1}_N - \mathbf{X} \mathbf{w} ||^2 \Big) \int \limits_\mathbb{R} \exp \Big( -\frac{n}{2\sigma^2} (\mu - \bar{y})^2 \Big) \ d \mu \\[6pt]
&\overset{\mathbf{y}}{\propto} \exp \Big( -\frac{1}{2\sigma^2} || \mathbf{y} - \bar{y} \mathbf{1}_N - \mathbf{X} \mathbf{w} ||^2 \Big) \int \limits_\mathbb{R} \text{N} \Big( \mu \Big| \bar{y}, \frac{\sigma^2}{n} \Big) \ d \mu \\[6pt]
&= \exp \Big( -\frac{1}{2\sigma^2} || \mathbf{y} - \bar{y} \mathbf{1}_N - \mathbf{X} \mathbf{w} ||^2 \Big). \\[6pt]
\end{aligned} \end{equation}$$
Thus, your posterior distribution is:
$$\mathbf{y}|\mathbf{X},\mathbf{w},\sigma^2 \sim \text{N}(\bar{y} \mathbf{1}_N - \mathbf{X} \mathbf{w}, \sigma^2).$$
