Conditional independence in posterior predictive distribution The derivation of posterior predictive distribution has the following steps - 
$\begin{split}p(\tilde y\mid y) = &\int p(\tilde y, \theta\mid y)~\mathsf d\theta \\ = &\int p(\tilde y\mid\theta, y)\,p(\theta\mid y)~\mathsf d\theta \\ = &\int p(\tilde y\mid\theta)\,p(\theta\mid y)~\mathsf d\theta\end{split}$
where 


*

*$\tilde y$ : new data for prediction

*$y$ : observed data

*$\theta$ : unknown parameter.


$p(\tilde y\mid\theta, y)$ reduces to $p(\tilde y\mid\theta)$ due to conditional independence. 
Can you explain why this is the case? The way I have convinced myself is that given $\tilde y$ is conditioned on $\theta$, the aspects of observed data $y$ is captured through $\theta$ and given $\tilde y\mid θ$ and $y$ are independent we can drop $y$ from the equation. 
Is there a more formal explanation for this? Also why doesn't this equation reduce to $p(\tilde y\mid θ) \cdot p(y)$?
 A: The unknown parameter $\theta$ is selected such that the prior and posterior data will be independent when its value is given.   That is such that $p(y,\tilde y\mid\theta)=p(y\mid \theta)\,p(\tilde y\mid\theta)$.
So, adding a few steps:
$\begin{align}p(\tilde y\mid y) = &\int p(\tilde y, \theta\mid y)~\mathsf d\theta &&\textsf{Law of Total Probability}\\[1ex] = &\int p(\tilde y\mid\theta, y)\,p(\theta\mid y)~\mathsf d\theta&&\textsf{Definition of Conditional Probability} \\[1ex]=&\int\dfrac{p(y,\tilde y\mid\theta)\;p(\theta\mid y)}{p(y\mid\theta)\hspace{10ex}}~\mathsf d\theta&&\textsf{Definition of Conditional Probability}\\[1ex]=& \int \dfrac{p(y\mid\theta)\,p(\tilde y\mid\theta)\;p(\theta\mid y)}{p(y\mid\theta)\hspace{16ex}}~\mathsf d\theta&&\text{Via the Conditional Independence} \\[1ex] = &\int p(\tilde y\mid\theta)\,p(\theta\mid y)~\mathsf d\theta && \text{Canceling the common factor.}\end{align}$
A: The 1st equality is true since through this fact: if $B_{1},B_{2},...,B_{n}$ partition A separately and independently then $P(A)=P(A\cap B_{1})+P(A\cap B_{2})+...+P(A\cap B_{n})$. The 2nd equality is the same conditioning through $P(x,y)=P(x)P(y|x)$ and the 3rd one is a Markov chain as ~y$\to\theta\to y$
