Learning continuous time Markov chains from sample executions

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Continuous-time Markov Chains (CTMCs) are an important class of stochastic models that have been used to model and analyze a variety of practical systems. In this paper we present an algorithm to learn and synthesize a CTMC model from sample executions of a system. Apart from its theoretical interest, we expect our algorithm to be useful in verifying black-box probabilistic systems and in compositionally verifying stochastic components interacting with unknown environments. We have implemented the algorithm and found it to be effective in learning CTMCs underlying practical systems from sample runs.

Original languageEnglish (US)
Title of host publicationProceedings - First International Conference on the Quantitative Evaluation of Systems, QEST 2004
PublisherIEEE Computer Society
Pages146-155
Number of pages10
ISBN (Print)0769521851, 9780769521855
DOIs
StatePublished - 2004
EventProceedings - First International Conference on the Quantitave Evaluation of Systems, QEST 2004 - Enschede, Netherlands
Duration: Sep 27 2004Sep 30 2004

Publication series

NameProceedings - First International Conference on the Quantitative Evaluation of Systems, QEST 2004

Other

OtherProceedings - First International Conference on the Quantitave Evaluation of Systems, QEST 2004
CountryNetherlands
CityEnschede
Period9/27/049/30/04

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint Dive into the research topics of 'Learning continuous time Markov chains from sample executions'. Together they form a unique fingerprint.

Cite this