How do I prove the following? I don't know where to start.
If $X$ is a random variable with $E^{\mathbb P}[X] = \mathbb P(X>0)=1$ and $ \mathbb Q$ is the probability measure defined by $ \mathbb Q(A)=E^{\mathbb Q}[X1_A] $ then $E^{\mathbb Q}[Y]=E^{\mathbb P}[XY]$
Use what David Williams calls the standard machine:
I guess $Y,X,XY \in \mathscr L ^{1}(\Omega, \mathscr F, \mathbb P)$, $Y,X,XY \in \mathscr L ^{1}(\Omega, \mathscr F, \mathbb Q)$ and $A \in \mathscr F$.
We want to show that $$E^{\mathbb Q}[Y]=E^{\mathbb P}[XY] \tag{*}$$
Doing this consists of four parts:
I think this is $$LHS(*) = \int_{\Omega} Y d \mathbb Q = \mathbb Q(A)$$
I think this is $$LHS(*) = \int_{\Omega} Y d \mathbb Q = a_1 \mathbb Q(A_1) + \cdots + a_n\mathbb Q(A_n)$$
I think this $$LHS(*) = \int_{\Omega} Y d \mathbb Q = \sup_{h \in SF^{+}, h \le Y}\{\int_{\Omega} h d \mathbb Q\}$$
I think this $$LHS(*) = \int_{\Omega} Y d \mathbb Q = \int_{\Omega} Y^{+} d \mathbb Q - \int_{\Omega} Y^{-} d \mathbb Q$$