Approximating Continuous Markov Processes
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Summary
This work shows how to approximate continuous systems by a countable family of nite-state probabilistic systems, how to reconstruct the full system from these nite approximant results, and shows that reasoning about properties deenable in a rich logic can be carried out in terms of the approximants.
- Type
- article
- Published
- 2000-01-01
- Cited by
- 11
- References
- 37
- OpenAlex
- https://openalex.org/W41021295
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:16887052
Keywords
Markov chain, Mathematics, Markov process, Computer science, Statistics
References
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Cited by
- Labeled Markov processes: stronger and faster approximations
- Concurrent constraint programming: towards probabilistic abstract interpretation
- Labelled Markov Processes: Stronger and Faster Approximations
- Trace-based process algebras for real-time probabilistic systems
- Processus de Markov étiquetés et Systèmes Hybrides probabilistes
- Application of reinforcement learning algorithms to software verification
- ε-Distance via Lévy-Prokhorov Lifting
- A Fixpoint Logic for Labeled Markov Processes
- Verification of Randomized Distributed Algorithms
- Continuous Time and/or Continuous Distributions
- Logical Relations for Monadic Types
- Conditional Expectation and the Approximation of Labelled Markov Processes