We aim to develop a theoretical framework, and efficient algorithms, for imprecise continuous-time Markov chains. The imprecision relates to the parameters of the model, which need not be specified exactly. This leads
to more robust and reliable results. The motivation stems from the popularity of traditional Markov chains, and from society’s increasing demand for features such as robustness and reliability.