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Data-efficient Bayesian verification of parametric Markov chains

Abstract:

Obtaining complete and accurate models for the formal verification of systems is often hard or impossible. We present a data-based verification approach, for properties expressed in a probabilistic logic, that addresses incomplete model knowledge. We obtain experimental data from a system that can be modelled as a parametric Markov chain. We propose a novel verification algorithm to quantify the confidence the underlying system satisfies a given property of interest by using this data. Given ...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted Manuscript

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Publisher copy:
10.1007/978-3-319-43425-4_3

Authors


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Institution:
University of Oxford
Department:
Oxford, MPLS, Computer Science
More by this author
Institution:
University of Oxford
Department:
Oxford, MPLS, Computer Science
Haesaert, S More by this author
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Institution:
University of Oxford
Department:
Oxford, MPLS, Computer Science
Publisher:
Springer Publisher's website
Volume:
9826
Pages:
35-51
Series:
Lecture Notes in Computer Science
Publication date:
2016
DOI:
ISSN:
0302-9743
URN:
uuid:7b4b4d51-021a-450a-be74-a9940e4654db
Source identifiers:
627193
Local pid:
pubs:627193
ISBN:
978-3-319-43424-7

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