  Recent work by Hermanns et al. and Kattenbelt et al. has extended
counterexample-guided abstraction refinement (CEGAR) to probabilistic programs.
These approaches are limited to predicate abstraction. We present a novel
technique, based on the abstract reachability tree recently introduced by
Gulavani et al., that can use arbitrary abstract domains and widening operators
(in the sense of abstract interpretation). We show how suitable widening
operators can deduce loop invariants diffcult to find for predicate
abstraction, and propose refinement techniques.
