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Moment Matching-Based Distribution Fitting with Generalized Hyper-Erlang Distributions

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Analytical and Stochastic Modeling Techniques and Applications (ASMTA 2013)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 7984))

Abstract

This paper describes a novel moment matching based fitting method for phase-type (PH) distributions. A special sub-class of phase-type distributions is introduced for the fitting, called generalized hyper-Erlang distributions. The user has to provide only two parameters: the number of moments to match, and the upper bound for the sum of the multiplicities of the eigenvalues of the distribution, which is related to the maximal size of the resulting PH distribution. Given these two parameters, our method obtains all PH distributions that match the target moments and have a Markovian representation up to the given size. From this set of PH distributions the best one can be selected according to any distance function.

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Author information

Authors and Affiliations

  1. Department of Networked Systems and Services, Budapest University of Technology and Economics, Hungary

    Gábor Horváth

  2. MTA-BME Information Systems Research Group, Magyar Tudósok krt. 2, 1117, Budapest, Hungary

    Gábor Horváth

  3. Inter-University Center of Telecommunications and Informatics, Hungary

    Gábor Horváth

Authors
  1. Gábor Horváth
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Editor information

Editors and Affiliations

  1. Belarusian State University, Nezavisimosti Ave. 4, 220030, Minsk, Belarus

    Alexander Dudin

  2. Department of Telecommunications and Information Processing, Ghent University, Sint-Pietersnieuwstraat 41, 9000, Gent, Belgium

    Koen De Turck

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© 2013 Springer-Verlag Berlin Heidelberg

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Horváth, G. (2013). Moment Matching-Based Distribution Fitting with Generalized Hyper-Erlang Distributions. In: Dudin, A., De Turck, K. (eds) Analytical and Stochastic Modeling Techniques and Applications. ASMTA 2013. Lecture Notes in Computer Science, vol 7984. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39408-9_17

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  • DOI: https://doi.org/10.1007/978-3-642-39408-9_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39407-2

  • Online ISBN: 978-3-642-39408-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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