  If the state space of a homogeneous continuous-time Markov chain is too
large, making inferences - here limited to determining marginal or limit
expectations - becomes computationally infeasible. Fortunately, the state space
of such a chain is usually too detailed for the inferences we are interested
in, in the sense that a less detailed - smaller - state space suffices to
unambiguously formalise the inference. However, in general this so-called
lumped state space inhibits computing exact inferences because the
corresponding dynamics are unknown and/or intractable to obtain. We address
this issue by considering an imprecise continuous-time Markov chain. In this
way, we are able to provide guaranteed lower and upper bounds for the
inferences of interest, without suffering from the curse of dimensionality.
