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Cluster-based fitting of phase-type distributions to empirical data

Lookup NU author(s): Dr Katinka Wolter


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We present a clustering-based fitting approach for phase-type distributions that is particularly suited to capture common characteristics of empirical data sets. The distributions fitted by this approach are especially useful in efficient simulation approaches. We describe the Hyper-* tool, which implements the algorithm and offers a user-friendly interface to efficient phase-type fitting. We provide a comparison of cluster-based fitting with segmentation-based approaches and other algorithms and show that clustering provides good results for typical empirical data sets.

Publication metadata

Author(s): Reinecke P, Krauss T, Wolter K

Publication type: Article

Publication status: Published

Journal: Computers & Mathematics with Application

Year: 2012

Volume: 64

Issue: 12

Pages: 3840-3851

Print publication date: 10/04/2012

ISSN (print): 0898-1221

ISSN (electronic): 1873-7668

Publisher: Pergamon


DOI: 10.1016/j.camwa.2012.03.016


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