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If the state space of a homogeneous continuous-time Markov chain is toolarge, making inferences - here limited to determining marginal or limitexpectations - becomes computationally infeasible. Fortunately, the state spaceof such a chain is usually too detailed for the inferences we are interestedin, in the sense that a less detailed - smaller - state space suffices tounambiguously formalise the inference. However, in general this so-calledlumped state space inhibits computing exact inferences because thecorresponding dynamics are unknown and/or intractable to obtain. We addressthis issue by considering an imprecise continuous-time Markov chain. In thisway, we are able to provide guaranteed lower and upper bounds for theinferences of interest, without suffering from the curse of dimensionality.

Computing Inferences for Large-Scale Continuous-Time Markov Chains by Combining Lumping with Imprecision