Probabilistic Programming Process Algebra

Anastasis Georgoulas; Jane Hillston; Dimitrios Milios; Guido Sanguinetti

doi:10.1007/978-3-319-10696-0_21

Probabilistic Programming Process Algebra

Anastasis Georgoulas, Jane Hillston, Dimitrios Milios, Guido Sanguinetti

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Quantitative Evaluation of Systems
Subtitle of host publication	11th International Conference, QEST 2014, Florence, Italy, September 8-10, 2014. Proceedings
Publisher	Springer
Pages	249-264
Number of pages	16
ISBN (Electronic)	978-3-319-10696-0
ISBN (Print)	978-3-319-10695-3
DOIs	https://doi.org/10.1007/978-3-319-10696-0_21
Publication status	Published - 8 Sept 2014
Event	11th International Conference on Quantit - Florence, Italy Duration: 8 Sept 2014 → 10 Sept 2014

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer International Publishing
Volume	8657
ISSN (Print)	0302-9743

Conference

Conference	11th International Conference on Quantit
Country/Territory	Italy
City	Florence
Period	8/09/14 → 10/09/14

Access to Document

10.1007/978-3-319-10696-0_21

QEST-ProPPAAccepted author manuscript, 838 KB

http://link.springer.com/chapter/10.1007%2F978-3-319-10696-0_21

QUANTICOL - A Quantitative Approach to Management and Design of Collective and Adaptive Behaviours (RTD)
Hillston, J. & Gilmore, S.
EU government bodies
1/04/13 → 31/03/17
Project: Research
Machine learning methods for formal dynamical systems: a systems biology case study
Hillston, J.
UK industry, commerce and public corporations
1/10/12 → 31/03/16
Project: Research
Systems Training in Maths, Informatics and Computational Biology (SySMIC)
Gilmore, S.
BBSRC
1/01/12 → 31/12/16
Project: Research

Cite this

@inproceedings{c0f54d5480fe4899bc08c17287c93997,

title = "Probabilistic Programming Process Algebra",

abstract = "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 incorporateobservational 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.",

author = "Anastasis Georgoulas and Jane Hillston and Dimitrios Milios and Guido Sanguinetti",

year = "2014",

month = sep,

day = "8",

doi = "10.1007/978-3-319-10696-0_21",

language = "English",

isbn = "978-3-319-10695-3",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "249--264",

booktitle = "Quantitative Evaluation of Systems",

note = "11th International Conference on Quantit ; Conference date: 08-09-2014 Through 10-09-2014",

}

Georgoulas, A , Hillston, J, Milios, D & Sanguinetti, G 2014, Probabilistic Programming Process Algebra. in Quantitative Evaluation of Systems: 11th International Conference, QEST 2014, Florence, Italy, September 8-10, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8657, Springer, pp. 249-264, 11th International Conference on Quantit, Florence, Italy, 8/09/14. https://doi.org/10.1007/978-3-319-10696-0_21

Probabilistic Programming Process Algebra. / Georgoulas, Anastasis ; Hillston, Jane; Milios, Dimitrios et al.
Quantitative Evaluation of Systems: 11th International Conference, QEST 2014, Florence, Italy, September 8-10, 2014. Proceedings. Springer, 2014. p. 249-264 (Lecture Notes in Computer Science; Vol. 8657).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Probabilistic Programming Process Algebra

AU - Georgoulas, Anastasis

AU - Hillston, Jane

AU - Milios, Dimitrios

AU - Sanguinetti, Guido

PY - 2014/9/8

Y1 - 2014/9/8

N2 - 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 incorporateobservational 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.

AB - 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 incorporateobservational 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.

U2 - 10.1007/978-3-319-10696-0_21

DO - 10.1007/978-3-319-10696-0_21

M3 - Conference contribution

SN - 978-3-319-10695-3

T3 - Lecture Notes in Computer Science

SP - 249

EP - 264

BT - Quantitative Evaluation of Systems

PB - Springer

T2 - 11th International Conference on Quantit

Y2 - 8 September 2014 through 10 September 2014

ER -

Probabilistic Programming Process Algebra

Abstract / Description of output

Publication series

Conference

Access to Document

Fingerprint

Projects

QUANTICOL - A Quantitative Approach to Management and Design of Collective and Adaptive Behaviours (RTD)

Machine learning methods for formal dynamical systems: a systems biology case study

Systems Training in Maths, Informatics and Computational Biology (SySMIC)

Cite this