  We provide a framework for speeding up algorithms for time-bounded
reachability analysis of continuous-time Markov decision processes. The
principle is to find a small, but almost equivalent subsystem of the original
system and only analyse the subsystem. Candidates for the subsystem are
identified through simulations and iteratively enlarged until runs are
represented in the subsystem with high enough probability. The framework is
thus dual to that of abstraction refinement. We instantiate the framework in
several ways with several traditional algorithms and experimentally confirm
orders-of-magnitude speed ups in many cases.
