I have a question regarding the support of an importance sampling distribution with respect to the support of the original distribution function. I was reading that the support of the importance sampling distribution (say the importance distribution is denoted q(x), say the set Q contains all value of x which satisfies q(x)>0 ) has to cover the support of the original distribution (say the distribution denoted as p(x) and let D denoted the set of x which satisfies p(x)>0 ), so it means we have $$D \subset Q$$.
The goal is to estimate some integral say:
$$\int_{D}f(x)p(x)dx$$
by using this instead:
$$\int_{Q} f(x) \frac{p(x)}{q(x)} q(x) dx $$
My question is that the author is saying q(x) doesn't have to be entirely bigger than zero as long as it is bigger than zero when $f(x)p(x)\neq 0$, i.e. $ x \in{Q}$ whenever $f(x)p(x) \neq 0$.
And next it goes about saying this:
$E_{q} \Bigg(\frac{f(X)p(x)}{q(x)} \Bigg)$ = $\int_{Q} \frac{f(x)p(x)}{q(x)} q(x) dx$ =
$\int_{D} f(x)p(x) dx$ + $\int_{Q \cap D^c} f(x)p(x) dx-\int_{D \cap Q^c} f(x)p(x) dx $
My question has to do with the very last line here, I don't really get why the expression on the very last line here would equal this:
$\int_{Q} \frac{f(x)p(x)}{q(x)} q(x) dx$.
It seems it is saying the set $Q$ is a union of the two sets: $D$ and $Q\cap D^c$. And $D\cap Q^c$ is the intersection of $D$ and $Q\cap D^c$.
My question is, is my understanding of the union and intersection correct regarding the above?
Because it seems it is based on the formula of:
$P(A\cup B) = P(A) + P(B) - P(A \cap B)$
If it is indeed based on the above simple "set union operation", then my question is how does the terms:
$\int_{D} f(x)p(x) dx$ + $\int_{Q \cap D^c} f(x)p(x) dx-\int_{D \cap Q^c} f(x)p(x) dx $
equal $\int_{Q} \frac{f(x)p(x)}{q(x)} q(x) dx$.
P.S. Hi Xian, yes it is here: http://statweb.stanford.edu/~owen/mc/Ch-var-is.pdf
It is just some notes I read from the pdf file.