2021/03/08 by Alberto Termine, Termine, Alberto, Alessandro Antonucci +5 · 1 citation
Computer Science · Mathematics · #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #FOS: Mathematics #Formal Methods in Verification #Logic (math.LO) #Logic in Computer Science (cs.LO) #Probability (math.PR) #Software Reliability and Analysis Research #cs.LO #math.LO #math.PR
paper · pdf · doi:10.48550/arxiv.2103.04841
Forthcoming in the proceedings of ISIPTA 2021 (International Symposium of Imprecise Probability: Theory and Applications)
openalex publication_date 2021/03/08 · arxiv created 2021/05/18 · arxiv updated 2021/05/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.