ArTS Archivio della ricerca di Trieste
ArTS è il sistema di gestione integrata dei dati della ricerca adottato dall' Università degli Studi di Trieste
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 | Bozza finale post-referaggio (post-print) | Digital Rights Management non definito | Open Access Visualizza/Apri |
| RV2015-2.pdf | pdf editoriale | Documento in Versione Editoriale | Digital Rights Management non definito | Administrator Richiedi una copia |
http://hdl.handle.net/11368/2860877
