  Because of the widespread diffusion of computational systems of stochastic
nature, in recent years probabilistic model checking has become an important
area of research. However, despite its great success, standard probabilistic
model checking suffers the limitation of requiring a sharp specification of the
probabilities governing the model behaviour. Imprecise probability theory
offers a natural approach to overcome such limitation by a sensitivity analysis
with respect to the values of these parameters. However, only extensions based
on discrete-time imprecise Markov chains have been considered so far for such a
robust approach to model checking. Here we present a further extension based on
imprecise Markov reward models. In particular, we derive efficient algorithms
to compute lower and upper bounds of the expected cumulative reward and
probabilistic bounded rewards based on existing results for imprecise Markov
chains. These ideas are finally tested on a real case study involving the
spend-down costs of geriatric medicine departments.
