Probabilistic Verification of Discrete Event Systems Using Acceptance Sampling
By Håkan L. S. Younes, Reid G. Simmons. International Conference on Computer Aided Verification, 2002
417 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/771412a4d7e76fc297874daad45a8063a77db3fb
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
- Marta Z. Kwiatkowska
- David Parker
- Gethin Norman
- Joost-Pieter Katoen
- Koushik Sen
- Christel Baier
- Gul A. Agha
- Monika Heiner
In Management Science and Operations Research
- David Parker
- Gethin Norman
- Marta Z. Kwiatkowska
- Monika Heiner
- Holger Hermanns
- Axel Legay
- Saddek Bensalem
- Robin Donaldson
Citations in the corpus
- The Ins and Outs of the Probabilistic Model Checker MRMC (2009, background): “This paper presents the tool’s current status and its implementation details.”
- Statistical Model Checking of Black-Box Probabilistic Systems (2004, methodology): “However, the Younes and Simmons' approach assumes that the system is controllable (not black-box) and can be used to generate sample executions from any state on need basis.”
- Model-Checking Algorithms for Continuous-Time Markov Chains (2002, methodology): “Other recent works on CSL are the extension to continuousspace Markov processes [30], the use of discrete-event simulation and hypothesis testing [ 72 ], and the use of Kronecker algebra to exploit the structure of the CTMC [20].”
- Statistical Model Checking: An Overview (2010, methodology): “Starting from time-bounded PCTL properties [34], the technique has been extended to handle properties with unbounded until operators [26], as well as to black-box systems [25,30].”
- Petri Nets for Systems and Synthetic Biology (2008, methodology): “Approximative model checking of CSL using discrete event simulation of probabilistic models has been proposed in [YS02] and implemented in the tool Ymer [YKNP06].”
- Heuristics for Scalable Dynamic Test Generation (2008, neutral): “Statistical model checking techniques [30, 25] verify probabilistic models against probabilistic properties approximately within probabilistic error bounds.”
- PRISM 4.0: Verification of Probabilistic Real-Time Systems (2011, methodology): “It also incorporates several new components, including engines for quantitative abstraction-refinement [9] and statistical model checking [14,5].”
- Approximate Probabilistic Model Checking (2004, methodology): “In [ 28 ], Younes and Simmons described a procedure for verifying properties of discrete event systems based on Monte-Carlo simulation and statistical hypothesis testing.”