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dc.contributor.authorAkar, N.en_US
dc.date.accessioned2016-02-08T10:03:50Z
dc.date.available2016-02-08T10:03:50Z
dc.date.issued2015en_US
dc.identifier.issn1532-6349
dc.identifier.urihttp://hdl.handle.net/11693/22714
dc.description.abstractA novel algorithmic method is proposed to fit matrix geometric distributions of desired order to empirical data or arbitrary discrete distributions. The proposed method effectively combines two existing approaches from two different disciplines: well-established model reduction methods used in system theory and moment matching methods of applied probability that employ second-order discrete phase-type distributions. The proposed approach is validated with exhaustive numerical examples including well-known statistical data. Copyrighten_US
dc.language.isoEnglishen_US
dc.source.titleStochastic Modelsen_US
dc.relation.isversionofhttp://dx.doi.org/10.1080/15326349.2014.1003271en_US
dc.subjectDiscrete phase type distributionen_US
dc.subjectMatrix geometric distributionen_US
dc.subjectModel reductionen_US
dc.subjectAlgorithmsen_US
dc.subjectGeometryen_US
dc.subjectMatrix algebraen_US
dc.subjectAlgorithmic methodsen_US
dc.subjectApplied probabilityen_US
dc.subjectDiscrete distributionen_US
dc.subjectMatrix-geometricen_US
dc.subjectModel reduction methoden_US
dc.subjectMoment matching methoden_US
dc.subjectProbability distributionsen_US
dc.titleFitting matrix geometric distributions by model reductionen_US
dc.typeArticleen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.citation.spage292en_US
dc.citation.epage315en_US
dc.citation.volumeNumber31en_US
dc.citation.issueNumber2en_US
dc.identifier.doi10.1080/15326349.2014.1003271en_US
dc.publisherTaylor and Francis Inc.en_US
dc.identifier.eissn1532-4214


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