I've just started working through the book, and I'm stuck with how the author handles conditional probability in (1.66).
The context is as follows. In this chapter we are working with a curve fitting task: we try to fit a polynomial $\sum w_ix^i$ to a training set $\{\mathbf {x}, \mathbf {t}\}$ with an assumption of Gaussian noise, i.e. for a single observed value $t$ for $x$, $p(t|x,\mathbf{w},\beta)=N(t|\sum w_ix^i,\beta^{-1})$, and assuming independence of data points, the likelihood is $p(\mathbf{t}|\mathbf{x},\mathbf{w},\beta)=\prod N(t_n|\sum w_ix^i,\beta^{-1}).$ Prior distribution of $\mathbf{w}$ is a multivariate Gaussian: $p(\mathbf{w}|\alpha)=N(\mathbf{0},\alpha^{-1}\mathbf{I})$.
Now, the author states: "Using Bayes’ theorem, the posterior distribution for w is proportional to the product of the prior distribution and the likelihood function:
$$p(\mathbf{w}|\mathbf{x},\mathbf{t},\alpha,\beta)\propto p(\mathbf{t}|\mathbf{x},\mathbf{w},\beta)p(\mathbf{w}|\alpha) \tag{1.66}$$
Later, this proportionality is used to maximize the probability on the left to obtain MAP value of $\mathbf{w}$, so significant factors cannot be simply omitted on the right.
This is where I'm stuck. The problem is I don't understand how he applies Bayes here. I tried to derive it, and that's what I've got:
$$p(\mathbf{w}|\mathbf{x},\mathbf{t},\alpha,\beta)\propto p(\mathbf{x},\mathbf{t},\mathbf{w},\alpha,\beta)=p(\mathbf{t}|\mathbf{x,w},\alpha,\beta)p(\mathbf{x,w},\alpha,\beta)\tag{A}$$
where the latter equals to $p(\mathbf{w}|\alpha,\beta,\mathbf{x})p(\alpha,\beta,\mathbf{x})$. I see that we can get rid of the second factor here because it is not really interesting if we want to maximize the expression — these are just model parameters or the data which is given. Also, I see that we can rewrite the first factor as $p(\mathbf{w}|\alpha)$. That's why: say we have $p(A|BC)\text{, then } p(A|BC)=\frac {p(ABC)}{p(BC)}=\frac {p(AB)p(C)}{p(B)p(C)}=p(A|B)$, which holds if both ($AB$ and $C$) and ($B$ and $C$) are independent, and indeed both $\mathbf{w}$ and $\alpha$ are independent with both $\beta\text{ and }\mathbf{x}$.
But I don't see why we can "remove" $\alpha$ from the first factor of (A). For one, $\mathbf{w}$ and $\alpha$ are not independent. Maybe I don't understand the problem well enough? Maybe this probability can be factorized so that we can say "this factor is irrelevant,let's hide it under $\propto$"?
So, the questions:
1) Please help with understanding of the proportionality (1.66).
2) It's hard for me to see the benefit of using conditional distributions on things like $\alpha$ and $\beta$. Is, for example, $p(\mathbf{w})$ of any interest in this task? I don't see how any meaning can be attributed to it. Could someone explain that?
3) Is there some common knowledge about conditioning on several random variables, like the one I used ($p(A|BC)=p(A|B)$ if both pairs ($AB$ and $C$) and ($B$ and $C$) are independent), that make it obvious?