  We describe stochastic calculus in the context of processes that are driven
by an adapted point process of locally finite intensity and are differentiable
between jumps. This includes Markov chains as well as non-Markov processes. By
analogy with It\^o processes we define the drift and diffusivity, which we then
use to describe a general sample path estimate. We then give several examples,
including ODE approximation, processes with linear drift, first passage times,
and an application to the stochastic logistic model.
