Bisimulation through probabilistic testing
Information and Computation
Process algebra for performance evaluation
Theoretical Computer Science
Process Algebras for Quantitative Analysis
LICS '05 Proceedings of the 20th Annual IEEE Symposium on Logic in Computer Science
Performance Analysis Using Stochastic Petri Nets
IEEE Transactions on Computers
Comparative branching-time semantics for Markov chains
Information and Computation
A Rigorous, Compositional, and Extensible Framework for Dynamic Fault Tree Analysis
IEEE Transactions on Dependable and Secure Computing
On Probabilistic Automata in Continuous Time
LICS '10 Proceedings of the 2010 25th Annual IEEE Symposium on Logic in Computer Science
Safety, Dependability and Performance Analysis of Extended AADL Models
The Computer Journal
Model-Based Engineering with AADL: An Introduction to the SAE Architecture Analysis & Design Language
Efficient modelling and generation of Markov automata
CONCUR'12 Proceedings of the 23rd international conference on Concurrency Theory
On the semantics of Markov automata
Information and Computation
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Treating random phenomena in concurrency theory has a long tradition. Petri nets [18,10] and process algebras [14] have been extended with probabilities. The same applies to behavioural semantics such as strong and weak (bi)simulation [1], and testing pre-orders [5]. Beautiful connections between probabilistic bisimulation [16] and Markov chain lumping [15] have been found. A plethora of probabilistic concurrency models has emerged [19]. Over the years, the focus shifted from covering discrete to treating continuous stochastic phenomena [12,13].