  In recent years probabilistic model checking has become an important area of
research because of the diffusion of computational systems of stochastic
nature. Despite its great success, standard probabilistic model checking
suffers the limitation of requiring a sharp specification of the probabilities
governing the model behaviour. The theory of imprecise probabilities 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. 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 tested on a real case study involving the spend-down
costs of geriatric medicine departments.
