Let $X_t$ be a continuous-time stochastic process on the state space {'off', 'on'}. Let $U$ be the cumulative duration spent 'on' during a time interval $[0,\tau]$ (that is, $U$ is the uptime during that interval).
Suppose that we simulate $U$ by sampling on an equispaced grid, as in:
- Choose $\delta t > 0$ such that $m = \tau / \delta t$ is an integer.
- Put $t_k = (k-1) \cdot \delta t$ for $k = 1 \dots m$.
- Put $Y_k = X_{t_k}$ for $k = 1 \dots m$.
- For any realization of $X_t$, put $V = (Y_1 + Y_2 + \dots Y_m) \cdot \delta t$.
Let $F(u) = Pr(U \leq u)$ be the cumulative distribution function for $U$ (intuitively, the process's cdf), and $G(u) = Pr(V \leq u)$ be the cumulative distribution function for $V$ (intuitively, the simulation's cdf).
When does $G$ converge (pointwise/uniformly) to $F$ as $\delta t \rightarrow 0$?
My attempt at an answer: A realization of $X_t$ will be misclassified if it has sojourns 'on' that are of duration less than $\delta t$. As $\delta t \rightarrow 0$, the probability of obtaining a misclassified realization converges to zero.
Many thanks in advance.