Projects per year
Abstract / Description of output
Formal languages like process algebras have been shown to be effective tools in modelling a wide range of dynamic systems, providing a high-level description that is readily transformed into an executable model. However their application is sometimes hampered because the quantitative details of many real-world systems of interest are not fully known. In contrast, in machine learning there has been work to develop probabilistic programming languages, which provide system descriptions that incorporate uncertainty and leverage advanced statistical techniques to infer unknown parameters from observed data. Unfortunately current probabilistic programming languages are typically too low-level to be suitable for complex modelling.
In this paper we present ProPPA, the first instance of the probabilistic programming paradigm being applied to a high-level, formal language, and its supporting tool suite. We explain the semantics of the language in terms of a quantitative generalisation of Constraint Markov Chains and describe the implementation of the language, discussing in some detail the different inference algorithms available, and their domain of applicability. We conclude by illustrating the use of the language on simple but non-trivial case studies: here ProPPA is shown to combine the elegance and simplicity of high-level formal modelling languages with an effective way of incorporating data, making it a promising tool for modelling studies.
In this paper we present ProPPA, the first instance of the probabilistic programming paradigm being applied to a high-level, formal language, and its supporting tool suite. We explain the semantics of the language in terms of a quantitative generalisation of Constraint Markov Chains and describe the implementation of the language, discussing in some detail the different inference algorithms available, and their domain of applicability. We conclude by illustrating the use of the language on simple but non-trivial case studies: here ProPPA is shown to combine the elegance and simplicity of high-level formal modelling languages with an effective way of incorporating data, making it a promising tool for modelling studies.
Original language | English |
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Article number | 3 |
Number of pages | 23 |
Journal | ACM Transactions on Modeling and Computer Simulation |
Volume | 28 |
Issue number | 1 |
DOIs | |
Publication status | Published - 23 Jan 2018 |
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Dive into the research topics of 'ProPPA: Probabilistic Programming for Stochastic Dynamical Systems'. Together they form a unique fingerprint.Projects
- 2 Finished
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QUANTICOL - A Quantitative Approach to Management and Design of Collective and Adaptive Behaviours (RTD)
1/04/13 → 31/03/17
Project: Research
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MLCS - Machine learning for computational science statistical and formal modeling of biological systems
Sanguinetti, G.
1/10/12 → 30/09/17
Project: Research
Profiles
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Jane Hillston
- School of Informatics - Personal Chair in Quantitative Modelling
- Laboratory for Foundations of Computer Science
- Data Science and Artificial Intelligence
Person: Academic: Research Active