The thing I'd stress is that, ultimately, the limit can be boiled down to $\lim_{x\to 0}\frac{\sin x}{x}=1$. How one computes that limit boils down to how sin(x) is defined.
suppose I take an Ehrenfest urn (which can be a seen as a discrete time markov chain on d + 1 states) and then make it a continuous time Markov chain by having it transition when a Poisson process with rate parameter d \lambda jumps. How do I compute the probability transition matrix P(t) of this continuous time markov chain?
Well the probability that the nth jump J_n = inf { t > 0 : N_t >= n} occurs at or before time t is the same as the probability that by time t, N_t >= n. i.e., it is 1 - P(N_t < n)
a completely different (and atrocious) approach is to write P(t) as P0 * probability that 0 jump have happened + P1 * probability that 1 jump has happened + P2 * probability that 2 jumps have happened + ..., where Pn is the transition matrix for n jumps
and then you are halfway through this replacing the probabilities that n jump have happened with their complicated expressions from the wikipedia page
that you realize you are writing down exactly the power series for exp(tQ)
@Hawk so the construction I've seen was something like this
Let $A_{i,n} = f^{-1}([\frac{i-1}{2^n},\frac{i}{2^n}))$
And let $B_n = f^{-1}([n,\infty))$
Then $s_n = \sum_{i=1}^{n2^n} \frac{i-1}{2^n}\chi_{A_{i,n}} + n\chi_{B_n}$
You may have seen something a little bit different, but at the end of the day you take preimages of certain intervals under $f$ and then linear combinations of characteristic functions of those intervals
The reason we need measurability of $f$ is that we require that if we write a simple function $s = \sum_{i=1}^n a_i\chi_{A_i}$, that the $A_i$ are measurable