Probabilistic Programming Process Algebra

Anastasis Georgoulas, Jane Hillston, Dimitrios Milios, Guido Sanguinetti

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

Abstract / Description of output

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.
Original languageEnglish
Title of host publicationQuantitative Evaluation of Systems
Subtitle of host publication11th International Conference, QEST 2014, Florence, Italy, September 8-10, 2014. Proceedings
PublisherSpringer International Publishing
Number of pages16
ISBN (Electronic)978-3-319-10696-0
ISBN (Print)978-3-319-10695-3
Publication statusPublished - 8 Sept 2014
Event11th International Conference on Quantit - Florence, Italy
Duration: 8 Sept 201410 Sept 2014

Publication series

NameLecture Notes in Computer Science
PublisherSpringer International Publishing
ISSN (Print)0302-9743


Conference11th International Conference on Quantit


Dive into the research topics of 'Probabilistic Programming Process Algebra'. Together they form a unique fingerprint.

Cite this