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EnglishRecent research has seen an increasingly fertile convergence of ideas from machine learning and formal modelling. Here we review some recently introduced methodologies for model checking and system design/parameter synthesis for logical properties against stochastic dynamical models. The crucial insight is a regularity result which states that the satisfaction probability of a logical formula is a smooth function of the parameters of a CTMC. This enables us to select an appropriate class of functional priors for Bayesian model checking and system design. We give a tutorial introduction to the statistical concepts, as well as an illustrative case study which demonstrates the usage of a newly-released software tool, U-check, which implements these methodologies.
| Titolo: | Machine Learning Methods in Statistical Model Checking and System Design – Tutorial |
| Autori: | |
| Data di pubblicazione: | 2015 |
| Serie: | |
| Abstract: | Recent research has seen an increasingly fertile convergence of ideas from machine learning and formal modelling. Here we review some recently introduced methodologies for model checking and system design/parameter synthesis for logical properties against stochastic dynamical models. The crucial insight is a regularity result which states that the satisfaction probability of a logical formula is a smooth function of the parameters of a CTMC. This enables us to select an appropriate class of functional priors for Bayesian model checking and system design. We give a tutorial introduction to the statistical concepts, as well as an illustrative case study which demonstrates the usage of a newly-released software tool, U-check, which implements these methodologies. |
| Handle: | http://hdl.handle.net/11368/2860877 |
| ISBN: | 9783319238197 9783319238203 |
| URL: | http://link.springer.com/bookseries/558 http://www.springer.com/gp/book/9783319238197 |
| Appare nelle tipologie: | 4.1 Contributo in Atti Convegno (Proceeding) |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| RV2015_tutorial-post.pdf | pdf post-print | Post-print | Digital Rights Management non definito | Open Access Visualizza/Apri |
| RV2015-2.pdf | pdf editoriale | Publisher-Version | Digital Rights Management non definito | Administrator Richiedi una copia |
http://hdl.handle.net/11368/2860877
