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Asymptotic Perturbation Bounds for Probabilistic Model Checking with Empirically Determined Probability Parameters
2016
IEEE Transactions on Software Engineering
Probabilistic model checking is a verification technique that has been the focus of intensive research for over a decade. One important issue with probabilistic model checking, which is crucial for its practical significance but is overlooked by the state-of-the-art largely, is the potential discrepancy between a stochastic model and the real-world system it represents when the model is built from statistical data. In the worst case, a tiny but nontrivial change to some model quantities might
doi:10.1109/tse.2015.2508444
fatcat:2lgw3vhyqrgatapyd2hrl4u5si