Bisimulation through probabilistic testing
Information and Computation
Model checking
Deciding bisimilarity and similarity for probabilistic processes
Journal of Computer and System Sciences
Model-checking continuous-time Markov chains
ACM Transactions on Computational Logic (TOCL)
Model Checking Performability Properties
DSN '02 Proceedings of the 2002 International Conference on Dependable Systems and Networks
Probabilistic Verification of Discrete Event Systems Using Acceptance Sampling
CAV '02 Proceedings of the 14th International Conference on Computer Aided Verification
Model-Checking Algorithms for Continuous-Time Markov Chains
IEEE Transactions on Software Engineering
Optimal state-space lumping in Markov chains
Information Processing Letters
Probabilistic weak simulation is decidable in polynomial time
Information Processing Letters
Model Checking Action- and State-Labelled Markov Chains
DSN '04 Proceedings of the 2004 International Conference on Dependable Systems and Networks
Probabilistic symbolic model checking with PRISM: a hybrid approach
International Journal on Software Tools for Technology Transfer (STTT) - Special section on tools and algorithms for the construction and analysis of systems
Model checking for a probabilistic branching time logic with fairness
Distributed Computing
Probabilistic model checking in practice: case studies with PRISM
ACM SIGMETRICS Performance Evaluation Review
Model Checking Markov Reward Models with Impulse Rewards
DSN '05 Proceedings of the 2005 International Conference on Dependable Systems and Networks
Model checking infinite-state markov chains
TACAS'05 Proceedings of the 11th international conference on Tools and Algorithms for the Construction and Analysis of Systems
QoS modelling and analysis with UML-statecharts: the StoCharts approach
ACM SIGMETRICS Performance Evaluation Review
Quantitative evaluation in embedded system design: trends in modeling and analysis techniques
Proceedings of the conference on Design, automation and test in Europe
Compositionality for Markov reward chains with fast and silent transitions
Performance Evaluation
INFAMY: An Infinite-State Markov Model Checker
CAV '09 Proceedings of the 21st International Conference on Computer Aided Verification
Simulation for interactive markov chains
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
ATVA'06 Proceedings of the 4th international conference on Automated Technology for Verification and Analysis
Approximate Verification of the Symbolic Dynamics of Markov Chains
LICS '12 Proceedings of the 2012 27th Annual IEEE/ACM Symposium on Logic in Computer Science
Electronic Notes in Theoretical Computer Science (ENTCS)
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Markov chains are one of the most popular models for the evaluation of performance and dependability of information processing systems. To obtain performance measures, typically long-run or transient state probabilities of Markov chains are determined. Sometimes the Markov chain at hand is equipped with rewards and computations involve determining long-run or instantaneous reward probabilities.This note summarises a technique to determine performance and dependability guarantees of Markov chains. Given a precise description of the desired guarantee, all states in the Markov chain are determined that surely meet the guarantee. This is done in a fully automated way. Guarantees are described using logics. The use of logics yields an expressive framework that allows to express well-known measures, but also (new) intricate and complex performance guarantees. The power of this technique is that no matter how complex the logical guarantee, it is automatically checked which states in the Markov chain satisfy it. Neither manual manipulations of Markov chains (or their high-level descriptions) are needed, nor the knowledge of any numerical technique to analyze them efficiently. This applies to any (time-homogeneous) Markov chain of any structure specified in any high-level formalism.