KL divergence and expectations

I am trying to understand the explanation of the KL divergence per below. It refers, as i understand it, to an expectation in the second term. "Approximating the expectation over q in this term". However, we are multiplying q(x) with the log of p(x) (rather than with p(x). Is it still correct to refer to this construct as an expected value? please let me know. • yes, it is the expected value of $log(p(x))$ under the distribution $q$ Dec 17 '15 at 22:49
• @seanv507 to clarify for future viewers: KL-divergence is the expected value of the difference between of information in mass functions p(x) and q(x) under the distribution q, i.e. E_q [(log(q(x)) - log(p(x))] = E_q [ I_q(x) - I_p(x) ] Mar 4 '19 at 11:41

Let $\Omega$ be the space of all possible outcomes and let $q:\Omega \rightarrow \mathbb{R}$ be a probability distribution defined on $\Omega$. For any function $f:\Omega \rightarrow S$ where $S$ is an arbitrary set that is closed under addition and scalar multiplication (e.g. $S = \mathbb{R}$) we can compute the expected value of $f$ under distribution $q$ as follows: $$\mathbb{E}[f] = \mathbb{E}_{x \sim q}[f(x)] = \sum_{x \in \Omega} q(x) f(x)$$
In the KL-divergence, we have that $f(x) = \ln{\frac{q(x)}{p(x)}}$ for some fixed $p(x)$.