  Imprecise continuous-time Markov chains are a robust type of continuous-time
Markov chains that allow for partially specified time-dependent parameters.
Computing inferences for them requires the solution of a non-linear
differential equation. As there is no general analytical expression for this
solution, efficient numerical approximation methods are essential to the
applicability of this model. We here improve the uniform approximation method
of Krak et al. (2016) in two ways and propose a novel and more efficient
adaptive approximation method. For ergodic chains, we also provide a method
that allows us to approximate stationary distributions up to any desired
maximal error.
