Let's say we are tossing two fair coins. Let $X$ be the random variable representing the number of heads.
$X$ is defined on $(\Omega, F, P)$ where $\Omega = \{HH,HT,TH,TT\}$.
Say if we want to find $E[X|G]$ where $G$ is a $\sigma$-algebra which contains the information about the first coin toss. It is clear $G=\{\emptyset, \Omega,\{HH,HT\},\{TT,TH\} \}$, where $G \subset F$. By definition, we know that $E[X|G]$ is a random variable mapping $\Omega \rightarrow \mathbb{R} $
Intuitively we know that the conditional expectation of X given the first toss is a $H$ is $1.5$, and the conditional expection of X given the first toss is a $T$ is $0.5$
However, when we are evaluating this quantity formally, using $E[X|G](\omega)$, wouldn't this mean we already know $\omega$? Why isn't $E[X|G](HH)$ simply $2$?
I do not understand why $E[X|G]$ takes the input $\omega \in \Omega$, instead of a set $s \in G$?
Any help is greatly appreaciated!
Just a thought (can someone confirm if this is correct?): By evaluating $E[X|G](HH)$ we do not actually know $\omega = HH$, but simply that $\omega \in \{HH,HT\}$ since all we are given is $G$
Let $Y$ denote the number of heads thrown at the first toss.
$\mathbb E[X\mid G]:\Omega\to\mathbb R$ has the following properties:
Combining the bullets we find that $$\mathbb Ef(Y)\mathbf1_B=\mathbb EX\mathbf1_B\text{ for every }B\in G$$
Substituting $B=\{HH,HT\}$ in the second bullet we find:$$\frac14f(1)+\frac14f(1)+\frac140+\frac140=\frac142+\frac141+\frac140+\frac140$$ telling us that $f(1)=\frac32$.
Substituting $B=\{TT,TH\}$ in the second bullet we find:$$\frac140+\frac140+\frac14f(0)+\frac14f(0)=\frac140+\frac140+\frac140+\frac141$$ telling us that $f(0)=\frac12$.
$\mathbb E[X\mid G]$ is defined for every $\omega\in\Omega$ and is prescribed as follows: