Compositional Markovian Modelling Using a Process Algebra

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We introduce a stochastic process algebra, PEPA, as a high-level modelling paradigm for continuous time Markov chains (CTMC). Process algebras are mathematical theories which model concurrent systems by their algebra and provide apparatus for reasoning about the structure and behaviour of the model. Recent extensions of these algebras, associating random variables with actions, make the models also amenable to Markovian analysis. A compositional structure is inherent in the PEPA language. As well as the clear advantages that this offers for model construction, we demonstrate how this compositionality may be exploited to reduce the state space of the CTMC. This leads to an exact aggregation based on lumpability.
Original languageEnglish
Title of host publicationComputations with Markov Chains
Subtitle of host publicationProceedings of the 2nd International Workshop on the Numerical Solution of Markov Chains
EditorsWilliam J. Stewart
Place of PublicationBoston, MA
PublisherSpringer US
Number of pages20
ISBN (Electronic)978-1-4615-2241-6
ISBN (Print)978-1-4613-5943-2
Publication statusPublished - 1995

Publication series

PublisherSpringer US


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