Let $(\Omega,\mathscr{F},P)$ be a probability space.
Let $X,Y$ be random variables on $\Omega$.
Then, we say $Z\sim X|Y$ iff (i) $\int_{Y^{-1}(A)} X dP = \int_{Y^{-1}(A)} Z dP$ and (ii) $Z$ is $\sigma(Y)$-measurable.
Now, let $S:\mathscr{\mathbb{R}} \times \Omega\rightarrow \mathbb{R}$ be a stochastic process.
What does it mean by $X|S$?
There are numerous papers saying like "... because $X|S \sim S$, $P(X\in A|S)= S(A)...$.
I think this is NOT actually a conditional expectation, but it is just a way to denote De Finneti theorem. Isn't it?
Note that $S$ can be seen as a measurable map $\Omega\rightarrow \prod_{A\in \mathscr{B}_{\mathbb{R}}} \mathbb{R}$. If the definition $X|S$ is consistent with the standard conditional expectation definition, $X|S$ is a random variable taking values in $\mathbb{R}$, same as $X$. However, since $X|S\sim S$, $X|S$ must take values in $\prod_{A\in \mathscr{B}_{\mathbb{R}}} \mathbb{R}$. Do you see inconsistency here?
This makes me confusing, so I am curious what's the definition of $X|S$.
What does $X|S$ mean?
** EDIT **
Here's the usage of this in "Theory of statistics - Mark J. Schervish"
As you can see here, the author says "$X_n$'s are independent and identically distributed as $P$ conditional on $\mathbb{P}=P$."
This means that $X|\mathbb{P}\sim \mathbb{P}$, which I do not get how to formally define it.
And
Last EDIT
The author says that it is a fact that $P(X\in A|\mathbb{P}=P)=P(A)$. So there must be another definition the author is referring to..