From this thread, a new question arose.
Given $(\Omega, \mathscr{F}, P)$ and a constant $c$, what's $E[X|X=c]$ and why?
I believe they suggested that $E[X|X=c]=c.$
I have however that for $B\in \mathscr{F},$
$$E(X|B)=\frac{E(X1_B)}{P(B)}.$$
If the distribution function of $X$ is continuous, $P(B)=P({X=x})=0!$
Actually, not sure about this since $P$ isn't a distribution function, but a measure. This article states:
When $P(H)=0$ (for instance if $Y$ is a [[continuous random variable]] and H is the event $Y=y$, this is in general the case), the [[Borel–Kolmogorov paradox]] demonstrates the ambiguity of attempting to define the conditional probability knowing the event $H$. The above formula shows that this problem transposes to the conditional expectation. So instead one only defines the conditional expectation with respect to a σ-algebra or a random variable.