2015
DOI: 10.1007/978-3-319-21690-4_13
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PROPhESY: A PRObabilistic ParamEter SYnthesis Tool

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Cited by 100 publications
(149 citation statements)
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“…When the range of uncertainty is not known, one can opt parametric-Markov models, that construct a symbolic model that can later be evaluated with specific probabilities. Recent work includes methods from parametric model checking [Hahn et al 2010;Filieri et al 2011a;Dehnert et al 2015].…”
Section: Dealing With Uncertainty In Control Strategies For Adaptationmentioning
confidence: 99%
“…When the range of uncertainty is not known, one can opt parametric-Markov models, that construct a symbolic model that can later be evaluated with specific probabilities. Recent work includes methods from parametric model checking [Hahn et al 2010;Filieri et al 2011a;Dehnert et al 2015].…”
Section: Dealing With Uncertainty In Control Strategies For Adaptationmentioning
confidence: 99%
“…The once-and-for-all IMC semantics ( [7,19,16]) is alike to the semantics for pMC, as introduced above. The associated satisfaction relation |= o I is defined as follows:…”
Section: Existing MC Abstraction Modelsmentioning
confidence: 99%
“…The original MC nand model has already been extended as a pMC in [7], where the authors consider a single parameter p for the probability that each of the N nand gates fails during the multiplexing. We extend this model to pIMC by considering intervals for the probability that the initial inputs are stimulated and we have one parameter per nand gate to represent the probability that it fails.…”
Section: Prototype Implementationmentioning
confidence: 99%
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