Formal languages like process algebras have been shown to be e ective 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 rst 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 di erent 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 e ective way of incorporating data, making it a promising tool for modelling studies. CCS Concepts: • Computing methodologies → Modeling methodologies; Uncertainty quanti cation; • Mathematics of computing → Markov processes; Bayesian computation; Markov-chain Monte Carlo methods;
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.