Model-checking continuous-time Markov chains
ACM Transactions on Computational Logic (TOCL)
On the Use of Model Checking Techniques for Dependability Evaluation
SRDS '00 Proceedings of the 19th IEEE Symposium on Reliable Distributed Systems
Model-Checking Algorithms for Continuous-Time Markov Chains
IEEE Transactions on Software Engineering
Numerical Methods for Structured Markov Chains (Numerical Mathematics and Scientific Computation)
Numerical Methods for Structured Markov Chains (Numerical Mathematics and Scientific Computation)
Almost-Sure Model Checking of Infinite Paths in One-Clock Timed Automata
LICS '08 Proceedings of the 2008 23rd Annual IEEE Symposium on Logic in Computer Science
Quantitative Model-Checking of One-Clock Timed Automata under Probabilistic Semantics
QEST '08 Proceedings of the 2008 Fifth International Conference on Quantitative Evaluation of Systems
Semi-Markov Chains and Hidden Semi-Markov Models toward Applications: Their Use in Reliability and DNA Analysis
Markov Chains and Stochastic Stability
Markov Chains and Stochastic Stability
Quantitative Model Checking of Continuous-Time Markov Chains Against Timed Automata Specifications
LICS '09 Proceedings of the 2009 24th Annual IEEE Symposium on Logic In Computer Science
Stochastic real-time games with qualitative timed automata objectives
CONCUR'10 Proceedings of the 21st international conference on Concurrency theory
Fixed-delay events in generalized semi-Markov processes revisited
CONCUR'11 Proceedings of the 22nd international conference on Concurrency theory
Observing continuous-time MDPs by 1-clock timed automata
RP'11 Proceedings of the 5th international conference on Reachability problems
Approximating acceptance probabilities of CTMC-paths on multi-clock deterministic timed automata
Proceedings of the 16th international conference on Hybrid systems: computation and control
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We propose deterministic timed automata (DTA) as a model-independent language for specifying performance and dependability measures over continuous-time stochastic processes. Technically, these measures are defined as limit frequencies of locations (control states) of a DTA that observes computations of a given stochastic process. Then, we study the properties of DTA measures over semi-Markov processes in greater detail. We show that DTA measures over semi-Markov processes are well-defined with probability one, and there are only finitely many values that can be assumed by these measures with positive probability. We also give an algorithm which approximates these values and the associated probabilities up to an arbitrarily small given precision. Thus, we obtain a general and effective framework for analysing DTA measures over semi-Markov processes.