Probabilistic Programming Process Algebra

  • Anastasis Georgoulas
  • Jane Hillston
  • Dimitrios Milios
  • Guido Sanguinetti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8657)


Formal modelling languages such as process algebras are widespread and effective tools in computational modelling. However, handling data and uncertainty in a statistically meaningful way is an open problem in formal modelling, severely hampering the usefulness of these elegant tools in many real world applications. Here we introduce ProPPA, a process algebra which incorporates uncertainty in the model description, allowing the use of Machine Learning techniques to incorporate observational information in the modelling. We define the semantics of the language by introducing a quantitative generalisation of Constraint Markov Chains. We present results from a prototype implementation of the language, demonstrating its usefulness in performing inference in a non-trivial example.


Model Check Inference Algorithm Label Transition System Approximate Bayesian Computation Process Algebra 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Anastasis Georgoulas
    • 1
  • Jane Hillston
    • 1
  • Dimitrios Milios
    • 1
  • Guido Sanguinetti
    • 1
    • 2
  1. 1.School of InformaticsUniversity of EdinburghUK
  2. 2.SynthSys — Synthetic and Systems BiologyUniversity of EdinburghUK

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