Parameter Synthesis for Markov Models: Covering the Parameter Space
Markov chain analysis is a key technique in formal verification. A practical obstacle is that all
probabilities in Markov models need to be known. However, system quantities such as …
probabilities in Markov models need to be known. However, system quantities such as …
Neural predictive monitoring
Abstract Neural State Classification (NSC) is a recently proposed method for runtime
predictive monitoring of Hybrid Automata (HA) using deep neural networks (DNNs). NSC …
predictive monitoring of Hybrid Automata (HA) using deep neural networks (DNNs). NSC …
Central limit model checking
We consider probabilistic model checking for continuous-time Markov chains (CTMCs)
induced from Stochastic Reaction Networks against a fragment of Continuous Stochastic …
induced from Stochastic Reaction Networks against a fragment of Continuous Stochastic …
The landing safety prediction model by integrating pattern recognition and Markov chain with flight data
S Zhou, Y Zhou, Z Xu, W Chang, Y Cheng - Neural Computing and …, 2019 - Springer
This paper aims to predict the landing state during the landing phase to ensure landing
safety and reduce the accidents loss. Some past researches have demonstrated the landing …
safety and reduce the accidents loss. Some past researches have demonstrated the landing …
Parametric statistical model checking of UAV flight plan
Abstract Unmanned Aerial Vehicles (UAV) are now widespread in our society and are often
used in a context where they can put people at risk. Studying their reliability, in particular in …
used in a context where they can put people at risk. Studying their reliability, in particular in …
Improving process algebra model structure and parameters in infectious disease epidemiology through data mining
Computational models are increasingly used to assist decision-making in public health
epidemiology, but achieving the best model is a complex task due to the interaction of many …
epidemiology, but achieving the best model is a complex task due to the interaction of many …
Conformal predictions for hybrid system state classification
Abstract Neural State Classification (NSC)[19] is a scalable method for the analysis of hybrid
systems, which consists in learning a neural network-based classifier able to detect whether …
systems, which consists in learning a neural network-based classifier able to detect whether …
Reachability design through approximate Bayesian computation
Time-bounded reachability problems are concerned with assessing whether a model's
trajectories traverse a given region of the state-space within given time-bounds. In the case …
trajectories traverse a given region of the state-space within given time-bounds. In the case …
Bayesian verification of chemical reaction networks
GW Molyneux, VB Wijesuriya, A Abate - International Symposium on …, 2019 - Springer
We present a data-driven verification approach that determines whether or not a given
chemical reaction network (CRN) satisfies a given property, expressed as a formula in a …
chemical reaction network (CRN) satisfies a given property, expressed as a formula in a …
Model checking approach to the analysis of biological systems
Formal verification techniques together with other computer science formal methods have
been recently tailored for applications to biological and biomedical systems. In contrast to …
been recently tailored for applications to biological and biomedical systems. In contrast to …