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Faster Statistical Model Checking by Means of Abstraction and Learning
[chapter]
2014
Lecture Notes in Computer Science
This paper investigates the combined use of abstraction and probabilistic learning as a means to enhance statistical model checking performance. We are given a property (or a list of properties) for verification on a (large) stochastic system. We project on a set of traces generated from the original system, and learn a (small) abstract model from the projected traces, which contain only those labels that are relevant to the property to be verified. Then, we model-check the property on the
doi:10.1007/978-3-319-11164-3_28
fatcat:ma4i5ji44vdmtbml3csmmkamdm