Central Limit Model Checking.
- Submitting institution
-
University of Oxford
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2054
- Type
- D - Journal article
- DOI
-
10.1145/3331452
- Title of journal
- ACM Transactions on Computational Logic
- Article number
- ARTN 19
- First page
- -
- Volume
- abs/1804.08744
- Issue
- 4
- ISSN
- 1529-3785
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- We describe probabilistic model checking for continuous-time Markov chains (CTMCs) that are induced by Stochastic Chemical Reaction Networks. Classical algorithms are limited to finite CTMCs and suffer from exponential growth of the state space. We employ a continuous-space approximation using a Gaussian process. We demonstrate that this overcomes the state space explosion problem, while still correctly characterizing stochastic behaviour. Our methods can be used for formal analysis of a wide range of systems, including biochemical networks, sensor networks, and population protocols. This work inspired a simulation tool that won the Best Tool Paper award at CMSB’2020 (https://link.springer.com/chapter/10.1007%2F978-3-030-60327-4_22).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
