The algorithmic analysis of hybrid systems
Theoretical Computer Science - Special issue on hybrid systems
Bisimulation for Labelled Markov Processes
LICS '97 Proceedings of the 12th Annual IEEE Symposium on Logic in Computer Science
Approximating Labeled Markov Processes
LICS '00 Proceedings of the 15th Annual IEEE Symposium on Logic in Computer Science
An approximation algorithm for labelled Markov processes: towards realistic approximation
QEST '05 Proceedings of the Second International Conference on the Quantitative Evaluation of Systems
HSCC '08 Proceedings of the 11th international workshop on Hybrid Systems: Computation and Control
Nondeterministic Labeled Markov Processes: Bisimulations and Logical Characterization
QEST '09 Proceedings of the 2009 Sixth International Conference on the Quantitative Evaluation of Systems
Approximating a behavioural pseudometric without discount for probabilistic systems
FOSSACS'07 Proceedings of the 10th international conference on Foundations of software science and computational structures
Testing probabilistic equivalence through reinforcement learning
FSTTCS'06 Proceedings of the 26th international conference on Foundations of Software Technology and Theoretical Computer Science
Safety verification for probabilistic hybrid systems
CAV'10 Proceedings of the 22nd international conference on Computer Aided Verification
Stochastic transition systems for continuous state spaces and non-determinism
FOSSACS'05 Proceedings of the 8th international conference on Foundations of Software Science and Computation Structures
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We compare two models of processes involving uncountable space. Labelled Markov processes are probabilistic transition systems that can have uncountably many states, but still make discrete time steps. The probability measures on the state space may have uncountable support. Hybrid processes are a combination of a continuous space process that evolves continuously with time and of a discrete component, such as a controller. Existing extensions of Hybrid processes with probability restrict the probabilistic behavior to the discrete component. We use an example of an aircraft to highlight the differences between the two models and we define a generalization of both that can model all the features of our aircraft example.